Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding...
Transcript of Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding...
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Week 10: Causality with Measured Confounding
Brandon Stewart1
Princeton
November 28 and 30, 2016
1These slides are heavily influenced by Matt Blackwell, Jens Hainmueller, ErinHartman, Kosuke Imai and Gary King.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 1 / 176
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Where We’ve Been and Where We’re Going...
Last WeekI regression diagnostics
This WeekI Monday:
F experimental IdealF identification with measured confounding
I Wednesday:F regression estimation
Next WeekI identification with unmeasured confoundingI instrumental variables
Long RunI causality with measured confounding → unmeasured confounding →
repeated data
Questions?
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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Lancet 2001: negative correlation between coronary heart disease mortalityand level of vitamin C in bloodstream (controlling for age, gender, bloodpressure, diabetes, and smoking)
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Lancet 2002: no effect of vitamin C on mortality in controlled placebo trial(controlling for nothing)
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Lancet 2003: comparing among individuals with the same age, gender,blood pressure, diabetes, and smoking, those with higher vitamin C levelshave lower levels of obesity, lower levels of alcohol consumption, are lesslikely to grow up in working class, etc.
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Why So Much Variation?
Confounders
X
T Y
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Why So Much Variation?
Confounders
X
T Y
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Observational Studies and Experimental Ideal
Randomization forms gold standard for causal inference, because itbalances observed and unobserved confounders
Cannot always randomize so we do observational studies, where weadjust for the observed covariates and hope that unobservables arebalanced
Better than hoping: design observational study to approximate anexperiment
I “The planner of an observational study should always ask himself: Howwould the study be conducted if it were possible to do it by controlledexperimentation” (Cochran 1965)
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Observational Studies and Experimental Ideal
Randomization forms gold standard for causal inference, because itbalances observed and unobserved confounders
Cannot always randomize so we do observational studies, where weadjust for the observed covariates and hope that unobservables arebalanced
Better than hoping: design observational study to approximate anexperiment
I “The planner of an observational study should always ask himself: Howwould the study be conducted if it were possible to do it by controlledexperimentation” (Cochran 1965)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 6 / 176
![Page 12: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/12.jpg)
Observational Studies and Experimental Ideal
Randomization forms gold standard for causal inference, because itbalances observed and unobserved confounders
Cannot always randomize so we do observational studies, where weadjust for the observed covariates and hope that unobservables arebalanced
Better than hoping: design observational study to approximate anexperiment
I “The planner of an observational study should always ask himself: Howwould the study be conducted if it were possible to do it by controlledexperimentation” (Cochran 1965)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 6 / 176
![Page 13: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/13.jpg)
Observational Studies and Experimental Ideal
Randomization forms gold standard for causal inference, because itbalances observed and unobserved confounders
Cannot always randomize so we do observational studies, where weadjust for the observed covariates and hope that unobservables arebalanced
Better than hoping: design observational study to approximate anexperiment
I “The planner of an observational study should always ask himself: Howwould the study be conducted if it were possible to do it by controlledexperimentation” (Cochran 1965)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 6 / 176
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Angrist and Pishke’s Frequently Asked Questions
What is the causal relationship of interest?
What is the experiment that could ideally be used to capture thecausal effect of interest?
What is your identification strategy?
What is your mode of statistical inference?
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Angrist and Pishke’s Frequently Asked Questions
What is the causal relationship of interest?
What is the experiment that could ideally be used to capture thecausal effect of interest?
What is your identification strategy?
What is your mode of statistical inference?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 7 / 176
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Angrist and Pishke’s Frequently Asked Questions
What is the causal relationship of interest?
What is the experiment that could ideally be used to capture thecausal effect of interest?
What is your identification strategy?
What is your mode of statistical inference?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 7 / 176
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Angrist and Pishke’s Frequently Asked Questions
What is the causal relationship of interest?
What is the experiment that could ideally be used to capture thecausal effect of interest?
What is your identification strategy?
What is your mode of statistical inference?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 7 / 176
![Page 18: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/18.jpg)
Angrist and Pishke’s Frequently Asked Questions
What is the causal relationship of interest?
What is the experiment that could ideally be used to capture thecausal effect of interest?
What is your identification strategy?
What is your mode of statistical inference?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 7 / 176
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Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
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Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.
I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
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Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
![Page 22: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/22.jpg)
Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
![Page 23: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/23.jpg)
Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
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Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
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Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
![Page 26: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/26.jpg)
Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.
I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
![Page 27: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/27.jpg)
Experiment review
An experiment is a study where assignment to treatment is controlledby the researcher.
I pi = P[Di = 1] be the probability of treatment assignment probability.I pi is controlled and known by researcher in an experiment.
A randomized experiment is an experiment with the followingproperties:
1 Positivity: assignment is probabilistic: 0 < pi < 1
I No deterministic assignment.
2 Unconfoundedness: P[Di = 1|Y(1),Y(0)] = P[Di = 1]
I Treatment assignment does not depend on any potential outcomes.I Sometimes written as Di⊥⊥(Y(1),Y(0))
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 8 / 176
![Page 28: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/28.jpg)
Why do Experiments Help?
Remember selection bias?
E [Yi |Di = 1]− E [Yi |Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1] + E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)− Yi (0)|Di = 1]︸ ︷︷ ︸Average Treatment Effect on Treated
+E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]︸ ︷︷ ︸selection bias
In an experiment we know that treatment is randomly assigned. Thus we can dothe following:
E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0] = E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1]
= E [Yi (1)]− E [Yi (0)]
When all goes well, an experiment eliminates selection bias.
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Why do Experiments Help?
Remember selection bias?
E [Yi |Di = 1]− E [Yi |Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1] + E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)− Yi (0)|Di = 1]︸ ︷︷ ︸Average Treatment Effect on Treated
+E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]︸ ︷︷ ︸selection bias
In an experiment we know that treatment is randomly assigned. Thus we can dothe following:
E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0] = E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1]
= E [Yi (1)]− E [Yi (0)]
When all goes well, an experiment eliminates selection bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 9 / 176
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Why do Experiments Help?
Remember selection bias?
E [Yi |Di = 1]− E [Yi |Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1] + E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)− Yi (0)|Di = 1]︸ ︷︷ ︸Average Treatment Effect on Treated
+E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]︸ ︷︷ ︸selection bias
In an experiment we know that treatment is randomly assigned. Thus we can dothe following:
E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0] = E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1]
= E [Yi (1)]− E [Yi (0)]
When all goes well, an experiment eliminates selection bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 9 / 176
![Page 31: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/31.jpg)
Why do Experiments Help?
Remember selection bias?
E [Yi |Di = 1]− E [Yi |Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1] + E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]
= E [Yi (1)− Yi (0)|Di = 1]︸ ︷︷ ︸Average Treatment Effect on Treated
+E [Yi (0)|Di = 1]− E [Yi (0)|Di = 0]︸ ︷︷ ︸selection bias
In an experiment we know that treatment is randomly assigned. Thus we can dothe following:
E [Yi (1)|Di = 1]− E [Yi (0)|Di = 0] = E [Yi (1)|Di = 1]− E [Yi (0)|Di = 1]
= E [Yi (1)]− E [Yi (0)]
When all goes well, an experiment eliminates selection bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 9 / 176
![Page 32: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/32.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness
, ignorability, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
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Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness
, ignorability, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
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Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness
, ignorability, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
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Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness
, ignorability, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 36: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/36.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness
, ignorability, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 37: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/37.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed X
I Also called: unconfoundedness
, ignorability, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 38: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/38.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness
, ignorability, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 39: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/39.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness, ignorability
, selection on observables,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 40: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/40.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness, ignorability, selection on observables
,no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 41: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/41.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness, ignorability, selection on observables,
no omitted variables
, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 42: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/42.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness, ignorability, selection on observables,
no omitted variables, exogenous
, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 43: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/43.jpg)
Observational studies
Many different sets of identification assumptions that we’ll cover.
To start, focus on studies that are similar to experiments, just withouta known and controlled treatment assignment.
I No guarantee that the treatment and control groups are comparable.
1 Positivity (Common Support): assignment is probabilistic:0 < P[Di = 1|X,Y(1),Y(0)] < 1
2 No unmeasured confounding: P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I For some observed XI Also called: unconfoundedness, ignorability, selection on observables,
no omitted variables, exogenous, conditionally exchangeable, etc.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 10 / 176
![Page 44: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/44.jpg)
Designing observational studies
Rubin (2008) argues that we should still “design” our observationalstudies:
I Pick the ideal experiment to this observational study.I Hide the outcome data.I Try to estimate the randomization procedure.I Analyze this as an experiment with this estimated procedure.
Tries to minimize “snooping” by picking the best modeling strategybefore seeing the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 11 / 176
![Page 45: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/45.jpg)
Designing observational studies
Rubin (2008) argues that we should still “design” our observationalstudies:
I Pick the ideal experiment to this observational study.
I Hide the outcome data.I Try to estimate the randomization procedure.I Analyze this as an experiment with this estimated procedure.
Tries to minimize “snooping” by picking the best modeling strategybefore seeing the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 11 / 176
![Page 46: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/46.jpg)
Designing observational studies
Rubin (2008) argues that we should still “design” our observationalstudies:
I Pick the ideal experiment to this observational study.I Hide the outcome data.
I Try to estimate the randomization procedure.I Analyze this as an experiment with this estimated procedure.
Tries to minimize “snooping” by picking the best modeling strategybefore seeing the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 11 / 176
![Page 47: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/47.jpg)
Designing observational studies
Rubin (2008) argues that we should still “design” our observationalstudies:
I Pick the ideal experiment to this observational study.I Hide the outcome data.I Try to estimate the randomization procedure.
I Analyze this as an experiment with this estimated procedure.
Tries to minimize “snooping” by picking the best modeling strategybefore seeing the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 11 / 176
![Page 48: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/48.jpg)
Designing observational studies
Rubin (2008) argues that we should still “design” our observationalstudies:
I Pick the ideal experiment to this observational study.I Hide the outcome data.I Try to estimate the randomization procedure.I Analyze this as an experiment with this estimated procedure.
Tries to minimize “snooping” by picking the best modeling strategybefore seeing the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 11 / 176
![Page 49: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/49.jpg)
Designing observational studies
Rubin (2008) argues that we should still “design” our observationalstudies:
I Pick the ideal experiment to this observational study.I Hide the outcome data.I Try to estimate the randomization procedure.I Analyze this as an experiment with this estimated procedure.
Tries to minimize “snooping” by picking the best modeling strategybefore seeing the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 11 / 176
![Page 50: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/50.jpg)
Discrete covariates
Suppose that we knew that Di was unconfounded within levels of abinary Xi .
Then we could always estimate the causal effect using iteratedexpectations as in a stratified randomized experiment:
EX
E[Yi |Di = 1,Xi ]− E[Yi |Di = 0,Xi ]
=(E[Yi |Di = 1,Xi = 1]− E[Yi |Di = 0,Xi = 1]
)︸ ︷︷ ︸
diff-in-means for Xi=1
P[Xi = 1]︸ ︷︷ ︸share of Xi=1
+(E[Yi |Di = 1,Xi = 0]− E[Yi |Di = 0,Xi = 0]
)︸ ︷︷ ︸
diff-in-means for Xi=0
P[Xi = 0]︸ ︷︷ ︸share of Xi=0
Never used our knowledge of the randomization for this quantity.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 12 / 176
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Discrete covariates
Suppose that we knew that Di was unconfounded within levels of abinary Xi .
Then we could always estimate the causal effect using iteratedexpectations as in a stratified randomized experiment:
EX
E[Yi |Di = 1,Xi ]− E[Yi |Di = 0,Xi ]
=(E[Yi |Di = 1,Xi = 1]− E[Yi |Di = 0,Xi = 1]
)︸ ︷︷ ︸
diff-in-means for Xi=1
P[Xi = 1]︸ ︷︷ ︸share of Xi=1
+(E[Yi |Di = 1,Xi = 0]− E[Yi |Di = 0,Xi = 0]
)︸ ︷︷ ︸
diff-in-means for Xi=0
P[Xi = 0]︸ ︷︷ ︸share of Xi=0
Never used our knowledge of the randomization for this quantity.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 12 / 176
![Page 52: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/52.jpg)
Discrete covariates
Suppose that we knew that Di was unconfounded within levels of abinary Xi .
Then we could always estimate the causal effect using iteratedexpectations as in a stratified randomized experiment:
EX
E[Yi |Di = 1,Xi ]− E[Yi |Di = 0,Xi ]
=(E[Yi |Di = 1,Xi = 1]− E[Yi |Di = 0,Xi = 1]
)︸ ︷︷ ︸
diff-in-means for Xi=1
P[Xi = 1]︸ ︷︷ ︸share of Xi=1
+(E[Yi |Di = 1,Xi = 0]− E[Yi |Di = 0,Xi = 0]
)︸ ︷︷ ︸
diff-in-means for Xi=0
P[Xi = 0]︸ ︷︷ ︸share of Xi=0
Never used our knowledge of the randomization for this quantity.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 12 / 176
![Page 53: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/53.jpg)
Discrete covariates
Suppose that we knew that Di was unconfounded within levels of abinary Xi .
Then we could always estimate the causal effect using iteratedexpectations as in a stratified randomized experiment:
EX
E[Yi |Di = 1,Xi ]− E[Yi |Di = 0,Xi ]
=(E[Yi |Di = 1,Xi = 1]− E[Yi |Di = 0,Xi = 1]
)︸ ︷︷ ︸
diff-in-means for Xi=1
P[Xi = 1]︸ ︷︷ ︸share of Xi=1
+(E[Yi |Di = 1,Xi = 0]− E[Yi |Di = 0,Xi = 0]
)︸ ︷︷ ︸
diff-in-means for Xi=0
P[Xi = 0]︸ ︷︷ ︸share of Xi=0
Never used our knowledge of the randomization for this quantity.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 12 / 176
![Page 54: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/54.jpg)
Discrete covariates
Suppose that we knew that Di was unconfounded within levels of abinary Xi .
Then we could always estimate the causal effect using iteratedexpectations as in a stratified randomized experiment:
EX
E[Yi |Di = 1,Xi ]− E[Yi |Di = 0,Xi ]
=(E[Yi |Di = 1,Xi = 1]− E[Yi |Di = 0,Xi = 1]
)︸ ︷︷ ︸
diff-in-means for Xi=1
P[Xi = 1]︸ ︷︷ ︸share of Xi=1
+(E[Yi |Di = 1,Xi = 0]− E[Yi |Di = 0,Xi = 0]
)︸ ︷︷ ︸
diff-in-means for Xi=0
P[Xi = 0]︸ ︷︷ ︸share of Xi=0
Never used our knowledge of the randomization for this quantity.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 12 / 176
![Page 55: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/55.jpg)
Discrete covariates
Suppose that we knew that Di was unconfounded within levels of abinary Xi .
Then we could always estimate the causal effect using iteratedexpectations as in a stratified randomized experiment:
EX
E[Yi |Di = 1,Xi ]− E[Yi |Di = 0,Xi ]
=(E[Yi |Di = 1,Xi = 1]− E[Yi |Di = 0,Xi = 1]
)︸ ︷︷ ︸
diff-in-means for Xi=1
P[Xi = 1]︸ ︷︷ ︸share of Xi=1
+(E[Yi |Di = 1,Xi = 0]− E[Yi |Di = 0,Xi = 0]
)︸ ︷︷ ︸
diff-in-means for Xi=0
P[Xi = 0]︸ ︷︷ ︸share of Xi=0
Never used our knowledge of the randomization for this quantity.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 12 / 176
![Page 56: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/56.jpg)
Stratification Example: Smoking and Mortality (Cochran,1968)
Table 1
Death Rates per 1,000 Person-Years
Smoking group Canada U.K. U.S.
Non-smokers 20.2 11.3 13.5Cigarettes 20.5 14.1 13.5Cigars/pipes 35.5 20.7 17.4
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 13 / 176
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Stratification Example: Smoking and Mortality (Cochran,1968)
Table 2
Mean Ages, Years
Smoking group Canada U.K. U.S.
Non-smokers 54.9 49.1 57.0Cigarettes 50.5 49.8 53.2Cigars/pipes 65.9 55.7 59.7
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 14 / 176
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Stratification
To control for differences in age, we would like to compare differentsmoking-habit groups with the same age distribution
One possibility is to use stratification:
for each country, divide each group into different age subgroups
calculate death rates within age subgroups
average within age subgroup death rates using fixed weights (e.g.number of cigarette smokers)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 15 / 176
![Page 59: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/59.jpg)
Stratification
To control for differences in age, we would like to compare differentsmoking-habit groups with the same age distribution
One possibility is to use stratification:
for each country, divide each group into different age subgroups
calculate death rates within age subgroups
average within age subgroup death rates using fixed weights (e.g.number of cigarette smokers)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 15 / 176
![Page 60: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/60.jpg)
Stratification
To control for differences in age, we would like to compare differentsmoking-habit groups with the same age distribution
One possibility is to use stratification:
for each country, divide each group into different age subgroups
calculate death rates within age subgroups
average within age subgroup death rates using fixed weights (e.g.number of cigarette smokers)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 15 / 176
![Page 61: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/61.jpg)
Stratification
To control for differences in age, we would like to compare differentsmoking-habit groups with the same age distribution
One possibility is to use stratification:
for each country, divide each group into different age subgroups
calculate death rates within age subgroups
average within age subgroup death rates using fixed weights (e.g.number of cigarette smokers)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 15 / 176
![Page 62: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/62.jpg)
Stratification
To control for differences in age, we would like to compare differentsmoking-habit groups with the same age distribution
One possibility is to use stratification:
for each country, divide each group into different age subgroups
calculate death rates within age subgroups
average within age subgroup death rates using fixed weights (e.g.number of cigarette smokers)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 15 / 176
![Page 63: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/63.jpg)
Stratification: Example
Death Rates # Pipe- # Non-Pipe Smokers Smokers Smokers
Age 20 - 50 15 11 29
Age 50 - 70 35 13 9
Age + 70 50 16 2
Total 40 40
What is the average death rate for Pipe Smokers?
15 · (11/40) + 35 · (13/40) + 50 · (16/40) = 35.5
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 16 / 176
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Stratification: Example
Death Rates # Pipe- # Non-Pipe Smokers Smokers Smokers
Age 20 - 50 15 11 29
Age 50 - 70 35 13 9
Age + 70 50 16 2
Total 40 40
What is the average death rate for Pipe Smokers?15 · (11/40) + 35 · (13/40) + 50 · (16/40) = 35.5
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 16 / 176
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Stratification: Example
Death Rates # Pipe- # Non-Pipe Smokers Smokers Smokers
Age 20 - 50 15 11 29
Age 50 - 70 35 13 9
Age + 70 50 16 2
Total 40 40
What is the average death rate for Pipe Smokers if they had same agedistribution as Non-Smokers?
15 · (29/40) + 35 · (9/40) + 50 · (2/40) = 21.2
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 17 / 176
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Stratification: Example
Death Rates # Pipe- # Non-Pipe Smokers Smokers Smokers
Age 20 - 50 15 11 29
Age 50 - 70 35 13 9
Age + 70 50 16 2
Total 40 40
What is the average death rate for Pipe Smokers if they had same agedistribution as Non-Smokers?15 · (29/40) + 35 · (9/40) + 50 · (2/40) = 21.2
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Smoking and Mortality (Cochran, 1968)
Table 3
Adjusted Death Rates using 3 Age groups
Smoking group Canada U.K. U.S.
Non-smokers 20.2 11.3 13.5Cigarettes 28.3 12.8 17.7Cigars/pipes 21.2 12.0 14.2
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 18 / 176
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Continuous covariates
So, great, we can stratify. Why not do this all the time?
What if Xi = income for unit i?
I Each unit has its own value of Xi : $54,134, $123,043, $23,842.I If Xi = 54134 is unique, will only observe 1 of these:
E[Yi |Di = 1,Xi = 54134]− E[Yi |Di = 0,Xi = 54134]
I cannot stratify to each unique value of Xi :
Practically, this is massively important: almost always have data withunique values.
One option is to discretize as we discussed with age, we will discuss morelater this week!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 19 / 176
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Continuous covariates
So, great, we can stratify. Why not do this all the time?
What if Xi = income for unit i?
I Each unit has its own value of Xi : $54,134, $123,043, $23,842.I If Xi = 54134 is unique, will only observe 1 of these:
E[Yi |Di = 1,Xi = 54134]− E[Yi |Di = 0,Xi = 54134]
I cannot stratify to each unique value of Xi :
Practically, this is massively important: almost always have data withunique values.
One option is to discretize as we discussed with age, we will discuss morelater this week!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 19 / 176
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Continuous covariates
So, great, we can stratify. Why not do this all the time?
What if Xi = income for unit i?
I Each unit has its own value of Xi : $54,134, $123,043, $23,842.
I If Xi = 54134 is unique, will only observe 1 of these:
E[Yi |Di = 1,Xi = 54134]− E[Yi |Di = 0,Xi = 54134]
I cannot stratify to each unique value of Xi :
Practically, this is massively important: almost always have data withunique values.
One option is to discretize as we discussed with age, we will discuss morelater this week!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 19 / 176
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Continuous covariates
So, great, we can stratify. Why not do this all the time?
What if Xi = income for unit i?
I Each unit has its own value of Xi : $54,134, $123,043, $23,842.I If Xi = 54134 is unique, will only observe 1 of these:
E[Yi |Di = 1,Xi = 54134]− E[Yi |Di = 0,Xi = 54134]
I cannot stratify to each unique value of Xi :
Practically, this is massively important: almost always have data withunique values.
One option is to discretize as we discussed with age, we will discuss morelater this week!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 19 / 176
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Continuous covariates
So, great, we can stratify. Why not do this all the time?
What if Xi = income for unit i?
I Each unit has its own value of Xi : $54,134, $123,043, $23,842.I If Xi = 54134 is unique, will only observe 1 of these:
E[Yi |Di = 1,Xi = 54134]− E[Yi |Di = 0,Xi = 54134]
I cannot stratify to each unique value of Xi :
Practically, this is massively important: almost always have data withunique values.
One option is to discretize as we discussed with age, we will discuss morelater this week!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 19 / 176
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Continuous covariates
So, great, we can stratify. Why not do this all the time?
What if Xi = income for unit i?
I Each unit has its own value of Xi : $54,134, $123,043, $23,842.I If Xi = 54134 is unique, will only observe 1 of these:
E[Yi |Di = 1,Xi = 54134]− E[Yi |Di = 0,Xi = 54134]
I cannot stratify to each unique value of Xi :
Practically, this is massively important: almost always have data withunique values.
One option is to discretize as we discussed with age, we will discuss morelater this week!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 19 / 176
![Page 74: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/74.jpg)
Continuous covariates
So, great, we can stratify. Why not do this all the time?
What if Xi = income for unit i?
I Each unit has its own value of Xi : $54,134, $123,043, $23,842.I If Xi = 54134 is unique, will only observe 1 of these:
E[Yi |Di = 1,Xi = 54134]− E[Yi |Di = 0,Xi = 54134]
I cannot stratify to each unique value of Xi :
Practically, this is massively important: almost always have data withunique values.
One option is to discretize as we discussed with age, we will discuss morelater this week!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 19 / 176
![Page 75: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/75.jpg)
Identification Under Selection on Observables
Identification Assumption1 (Y1,Y0)⊥⊥D|X (selection on observables)
2 0 < Pr(D = 1|X ) < 1 with probability one (common support)
Identification ResultGiven selection on observables we have
E[Y1 − Y0|X ] = E[Y1 − Y0|X ,D = 1]
= E[Y |X ,D = 1]− E[Y |X ,D = 0]
Therefore, under the common support condition:
τATE = E[Y1 − Y0] =
∫E[Y1 − Y0|X ] dP(X )
=
∫ (E[Y |X ,D = 1]− E[Y |X ,D = 0]
)dP(X )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 20 / 176
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Identification Under Selection on Observables
Identification Assumption1 (Y1,Y0)⊥⊥D|X (selection on observables)
2 0 < Pr(D = 1|X ) < 1 with probability one (common support)
Identification ResultSimilarly,
τATT = E[Y1 − Y0|D = 1]
=
∫ (E[Y |X ,D = 1]− E[Y |X ,D = 0]
)dP(X |D = 1)
To identify τATT the selection on observables and common support conditions canbe relaxed to:
Y0⊥⊥D|X (SOO for Controls)
Pr(D = 1|X ) < 1 (Weak Overlap)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 20 / 176
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Identification Under Selection on Observables
Potential Outcome Potential Outcomeunit under Treatment under Control
i Y1i Y0i Di Xi
1 E[Y1|X = 0,D = 1] E[Y0|X = 0,D = 1]1 0
2 1 03 E[Y1|X = 0,D = 0] E[Y0|X = 0,D = 0]
0 04 0 05 E[Y1|X = 1,D = 1] E[Y0|X = 1,D = 1]
1 16 1 17 E[Y1|X = 1,D = 0] E[Y0|X = 1,D = 0]
0 18 0 1
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 21 / 176
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Identification Under Selection on Observables
Potential Outcome Potential Outcomeunit under Treatment under Control
i Y1i Y0i Di Xi
1 E[Y1|X = 0,D = 1]E[Y0|X = 0,D = 1]= 1 0
2 E[Y0|X = 0,D = 0] 1 03 E[Y1|X = 0,D = 0] E[Y0|X = 0,D = 0]
0 04 0 05 E[Y1|X = 1,D = 1]
E[Y0|X = 1,D = 1]= 1 16 E[Y0|X = 1,D = 0] 1 17 E[Y1|X = 1,D = 0] E[Y0|X = 1,D = 0]
0 18 0 1
(Y1,Y0)⊥⊥D|X implies that we conditioned on all confounders. The treat-ment is randomly assigned within each stratum of X :
E[Y0|X = 0,D = 1] = E[Y0|X = 0,D = 0] and
E[Y0|X = 1,D = 1] = E[Y0|X = 1,D = 0]
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 21 / 176
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Identification Under Selection on Observables
Potential Outcome Potential Outcomeunit under Treatment under Control
i Y1i Y0i Di Xi
1 E[Y1|X = 0,D = 1]E[Y0|X = 0,D = 1]= 1 0
2 E[Y0|X = 0,D = 0] 1 03 E[Y1|X = 0,D = 0] = E[Y0|X = 0,D = 0]
0 04 E[Y1|X = 0,D = 1] 0 05 E[Y1|X = 1,D = 1]
E[Y0|X = 1,D = 1]= 1 16 E[Y0|X = 1,D = 0] 1 17 E[Y1|X = 1,D = 0] = E[Y0|X = 1,D = 0]
0 18 E[Y1|X = 1,D = 1] 0 1
(Y1,Y0)⊥⊥D|X also implies
E[Y1|X = 0,D = 1] = E[Y1|X = 0,D = 0] and
E[Y1|X = 1,D = 1] = E[Y1|X = 1,D = 0]
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 21 / 176
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What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)I effect of job training program on employment (confounding:
motivation)I effect of political institutions on economic development (confounding:
previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
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What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)I effect of job training program on employment (confounding:
motivation)I effect of political institutions on economic development (confounding:
previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
![Page 82: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/82.jpg)
What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)I effect of job training program on employment (confounding:
motivation)I effect of political institutions on economic development (confounding:
previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
![Page 83: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/83.jpg)
What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)I effect of job training program on employment (confounding:
motivation)I effect of political institutions on economic development (confounding:
previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
![Page 84: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/84.jpg)
What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)
I effect of job training program on employment (confounding:motivation)
I effect of political institutions on economic development (confounding:previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
![Page 85: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/85.jpg)
What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)I effect of job training program on employment (confounding:
motivation)
I effect of political institutions on economic development (confounding:previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
![Page 86: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/86.jpg)
What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)I effect of job training program on employment (confounding:
motivation)I effect of political institutions on economic development (confounding:
previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
![Page 87: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/87.jpg)
What is confounding?
Confounding is the bias caused by common causes of the treatmentand outcome.
I Leads to “spurious correlation.”
In observational studies, the goal is to avoid confounding inherent inthe data.
Pervasive in the social sciences:
I effect of income on voting (confounding: age)I effect of job training program on employment (confounding:
motivation)I effect of political institutions on economic development (confounding:
previous economic development)
No unmeasured confounding assumes that we’ve measured all sourcesof confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 22 / 176
![Page 88: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/88.jpg)
Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]I Include covariates such that, conditional on them, the treatment
assignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
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Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]I Include covariates such that, conditional on them, the treatment
assignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
![Page 90: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/90.jpg)
Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?
I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]I Include covariates such that, conditional on them, the treatment
assignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
![Page 91: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/91.jpg)
Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]I Include covariates such that, conditional on them, the treatment
assignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
![Page 92: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/92.jpg)
Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]I Include covariates such that, conditional on them, the treatment
assignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
![Page 93: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/93.jpg)
Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]
I Include covariates such that, conditional on them, the treatmentassignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
![Page 94: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/94.jpg)
Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]I Include covariates such that, conditional on them, the treatment
assignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
![Page 95: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/95.jpg)
Big problem
How can we determine if no unmeasured confounding holds if wedidn’t assign the treatment?
Put differently:
I What covariates do we need to condition on?I What covariates do we need to include in our regressions?
One way, from the assumption itself:
I P[Di = 1|X,Y(1),Y(0)] = P[Di = 1|X]I Include covariates such that, conditional on them, the treatment
assignment does not depend on the potential outcomes.
Another way: use DAGs and look at back-door paths.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 23 / 176
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Backdoor paths and blocking paths
Backdoor path: is a non-causal path from D to Y .
I Would remain if we removed any arrows pointing out of D.
Backdoor paths between D and Y common causes of D and Y :
D
X
Y
Here there is a backdoor path D ← X → Y , where X is a commoncause for the treatment and the outcome.
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Backdoor paths and blocking paths
Backdoor path: is a non-causal path from D to Y .
I Would remain if we removed any arrows pointing out of D.
Backdoor paths between D and Y common causes of D and Y :
D
X
Y
Here there is a backdoor path D ← X → Y , where X is a commoncause for the treatment and the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 24 / 176
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Backdoor paths and blocking paths
Backdoor path: is a non-causal path from D to Y .
I Would remain if we removed any arrows pointing out of D.
Backdoor paths between D and Y common causes of D and Y :
D
X
Y
Here there is a backdoor path D ← X → Y , where X is a commoncause for the treatment and the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 24 / 176
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Backdoor paths and blocking paths
Backdoor path: is a non-causal path from D to Y .
I Would remain if we removed any arrows pointing out of D.
Backdoor paths between D and Y common causes of D and Y :
D
X
Y
Here there is a backdoor path D ← X → Y , where X is a commoncause for the treatment and the outcome.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 24 / 176
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Other types of confounding
D
U X
Y
D is enrolling in a job training program.
Y is getting a job.
U is being motivated
X is number of job applications sent out.
Big assumption here: no arrow from U to Y .
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Other types of confounding
D
U X
Y
D is enrolling in a job training program.
Y is getting a job.
U is being motivated
X is number of job applications sent out.
Big assumption here: no arrow from U to Y .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 25 / 176
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Other types of confounding
D
U X
Y
D is enrolling in a job training program.
Y is getting a job.
U is being motivated
X is number of job applications sent out.
Big assumption here: no arrow from U to Y .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 25 / 176
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Other types of confounding
D
U X
Y
D is enrolling in a job training program.
Y is getting a job.
U is being motivated
X is number of job applications sent out.
Big assumption here: no arrow from U to Y .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 25 / 176
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Other types of confounding
D
U X
Y
D is enrolling in a job training program.
Y is getting a job.
U is being motivated
X is number of job applications sent out.
Big assumption here: no arrow from U to Y .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 25 / 176
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Other types of confounding
D
U X
Y
D is exercise.
Y is having a disease.
U is lifestyle.
X is smoking
Big assumption here: no arrow from U to Y .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 26 / 176
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What’s the problem with backdoor paths?
D
U X
Y
A path is blocked if:
1 we control for or stratify a non-collider on that path OR2 we do not control for a collider.
Unblocked backdoor paths confounding.
In the DAG here, if we condition on X , then the backdoor path isblocked.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 27 / 176
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What’s the problem with backdoor paths?
D
U X
Y
A path is blocked if:
1 we control for or stratify a non-collider on that path OR
2 we do not control for a collider.
Unblocked backdoor paths confounding.
In the DAG here, if we condition on X , then the backdoor path isblocked.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 27 / 176
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What’s the problem with backdoor paths?
D
U X
Y
A path is blocked if:
1 we control for or stratify a non-collider on that path OR2 we do not control for a collider.
Unblocked backdoor paths confounding.
In the DAG here, if we condition on X , then the backdoor path isblocked.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 27 / 176
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What’s the problem with backdoor paths?
D
U X
Y
A path is blocked if:
1 we control for or stratify a non-collider on that path OR2 we do not control for a collider.
Unblocked backdoor paths confounding.
In the DAG here, if we condition on X , then the backdoor path isblocked.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 27 / 176
![Page 110: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/110.jpg)
What’s the problem with backdoor paths?
D
U X
Y
A path is blocked if:
1 we control for or stratify a non-collider on that path OR2 we do not control for a collider.
Unblocked backdoor paths confounding.
In the DAG here, if we condition on X , then the backdoor path isblocked.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 27 / 176
![Page 111: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/111.jpg)
Not all backdoor paths
D
U1
X
Y
Conditioning on the posttreatment covariates opens the non-causalpath.
I selection bias.
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Not all backdoor paths
D
U1
X
Y
Conditioning on the posttreatment covariates opens the non-causalpath.
I selection bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 28 / 176
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Not all backdoor paths
D
U1
X
Y
Conditioning on the posttreatment covariates opens the non-causalpath.
I selection bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 28 / 176
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Don’t condition on post-treatment variables
Every time you do, a puppy cries.
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Don’t condition on post-treatment variables
Every time you do, a puppy cries.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 29 / 176
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M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
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M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
![Page 118: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/118.jpg)
M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
![Page 119: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/119.jpg)
M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
![Page 120: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/120.jpg)
M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
![Page 121: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/121.jpg)
M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
![Page 122: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/122.jpg)
M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.
I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
![Page 123: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/123.jpg)
M-bias
D
U1 U2
X
Y
Not all backdoor paths induce confounding.
This backdoor path is blocked by the collider X that we don’t controlfor.
If we control for X opens the path and induces confounding.
I Sometimes called M-bias.
Controversial because of differing views on what to control for:
I Rubin thinks that M-bias is a “mathematical curiosity” and we shouldcontrol for all pretreatment variables
I Pearl and others think M-bias is a real threat.I See the Elwert and Winship piece for more!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 30 / 176
![Page 124: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/124.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
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Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
![Page 126: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/126.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR
2 Measured covariates are sufficient to block all backdoor paths from Dto Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
![Page 127: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/127.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
![Page 128: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/128.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
![Page 129: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/129.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
![Page 130: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/130.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,
I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
![Page 131: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/131.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, and
I what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
![Page 132: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/132.jpg)
Backdoor criterion
Can we use a DAG to evaluate no unmeasured confounders?
Pearl answered yes, with the backdoor criterion, which states that theeffect of D on Y is identified if:
1 No backdoor paths from D to Y OR2 Measured covariates are sufficient to block all backdoor paths from D
to Y .
First is really only valid for randomized experiments.
The backdoor criterion is fairly powerful. Tells us:
I if there is confounding given this DAG,I if it is possible to remove the confounding, andI what variables to condition on to eliminate the confounding.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 31 / 176
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Example: Sufficient Conditioning Sets
U1
U3
Z1 Z2 Z3
X
Z5
Y
U9
U11
Z4
U2
U5
U4
U6
U7
U10
U8
Remove arrows out of X .Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 32 / 176
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Example: Sufficient Conditioning Sets
U1
U3
Z1 Z2 Z3
X
Z5
Y
U9
U11
Z4
U2
U5
U4
U6
U7
U10
U8
Recall that paths are blocked by “unconditioned colliders” or conditioned non-colliders
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 33 / 176
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Example: Sufficient Conditioning Sets
U1
U3
Z1 Z2 Z3
X
Z5
Y
U9
U11
Z4
U2
U5
U4
U6
U7
U10
U8
Recall that paths are blocked by “unconditioned colliders” or conditioned non-colliders
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 34 / 176
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Example: Sufficient Conditioning Sets
U1
U3
Z1 Z2 Z3
X
Z5
Y
U9
U11
Z4
U2
U5
U4
U6
U7
U10
U8
Recall that paths are blocked by “unconditioned colliders” or conditioned non-colliders
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 35 / 176
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Example: Sufficient Conditioning Sets
U1
U3
Z1 Z2 Z3
X
Z5
Y
U9
U11
Z4
U2
U5
U4
U6
U7
U10
U8
Recall that paths are blocked by “unconditioned colliders” or conditioned non-colliders
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 36 / 176
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Example: Non-sufficient Conditioning Sets
U1
U3
Z1 Z2 Z3
X
Z5
Y
U9
U11
Z4
U2
U5
U4
U6
U7
U10
U8
Recall that paths are blocked by “unconditioned colliders” or conditioned non-colliders
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 37 / 176
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Example: Non-sufficient Conditioning Sets
U1
U3
Z1 Z2 Z3
X
Z5
Y
U9
U11
Z4
U2
U5
U4
U6
U7
U10
U8
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 38 / 176
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Implications (via Vanderweele and Shpitser 2011)
1 Choose all pre-treatment covariates
(would condition on C2 inducing M-bias)
2 Choose all covariates which directly cause the treatment and the outcome
(would leave open a backdoor path A← C3 ← U3 → Y .)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 39 / 176
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Implications (via Vanderweele and Shpitser 2011)
Two common criteria fail here:
1 Choose all pre-treatment covariates
(would condition on C2 inducing M-bias)
2 Choose all covariates which directly cause the treatment and the outcome
(would leave open a backdoor path A← C3 ← U3 → Y .)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 39 / 176
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Implications (via Vanderweele and Shpitser 2011)
Two common criteria fail here:
1 Choose all pre-treatment covariates(would condition on C2 inducing M-bias)
2 Choose all covariates which directly cause the treatment and the outcome
(would leave open a backdoor path A← C3 ← U3 → Y .)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 39 / 176
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Implications (via Vanderweele and Shpitser 2011)
Two common criteria fail here:
1 Choose all pre-treatment covariates(would condition on C2 inducing M-bias)
2 Choose all covariates which directly cause the treatment and the outcome(would leave open a backdoor path A← C3 ← U3 → Y .)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 39 / 176
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SWIGs
D
U X
Y
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independentI Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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SWIGs
D
U X
Y
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independentI Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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SWIGs
D
U X
Y
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independentI Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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SWIGs
D | d Y (d)
U X
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .
I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independentI Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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SWIGs
D | d Y (d)
U X
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independentI Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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SWIGs
D | d Y (d)
U X
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independentI Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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SWIGs
D | d Y (d)
U X
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independent
I Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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SWIGs
D | d Y (d)
U X
It’s a little hard to see how the backdoor criterion implies nounmeasured confounders.
I No potential outcomes on this graph!
Richardson and Robins: Single World Intervention Graphs
I Split D node into natural value (D) and intervention value d .I Let all effects of D take their potential value under intervention Y (d).
Now can see: are D and Y (d) related?
I D ← U → X → Y (d) implies not independentI Conditioning on X blocks that backdoor path D⊥⊥Y (d)|X
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 40 / 176
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No unmeasured confounders is not testable
No unmeasured confounding places no restrictions on the observeddata. (
Yi (0)∣∣Di = 1,Xi
)︸ ︷︷ ︸unobserved
d=(Yi (0)
∣∣Di = 0,Xi
)︸ ︷︷ ︸observed
Here,d= means equal in distribution.
No way to directly test this assumption without the counterfactualdata, which is missing by definition!
With backdoor criterion, you must have the correct DAG.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 41 / 176
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No unmeasured confounders is not testable
No unmeasured confounding places no restrictions on the observeddata. (
Yi (0)∣∣Di = 1,Xi
)︸ ︷︷ ︸unobserved
d=(Yi (0)
∣∣Di = 0,Xi
)︸ ︷︷ ︸observed
Here,d= means equal in distribution.
No way to directly test this assumption without the counterfactualdata, which is missing by definition!
With backdoor criterion, you must have the correct DAG.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 41 / 176
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No unmeasured confounders is not testable
No unmeasured confounding places no restrictions on the observeddata. (
Yi (0)∣∣Di = 1,Xi
)︸ ︷︷ ︸unobserved
d=(Yi (0)
∣∣Di = 0,Xi
)︸ ︷︷ ︸observed
Here,d= means equal in distribution.
No way to directly test this assumption without the counterfactualdata, which is missing by definition!
With backdoor criterion, you must have the correct DAG.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 41 / 176
![Page 155: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/155.jpg)
No unmeasured confounders is not testable
No unmeasured confounding places no restrictions on the observeddata. (
Yi (0)∣∣Di = 1,Xi
)︸ ︷︷ ︸unobserved
d=(Yi (0)
∣∣Di = 0,Xi
)︸ ︷︷ ︸observed
Here,d= means equal in distribution.
No way to directly test this assumption without the counterfactualdata, which is missing by definition!
With backdoor criterion, you must have the correct DAG.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 41 / 176
![Page 156: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/156.jpg)
Assessing no unmeasured confounders
Can do “placebo” tests, where Di cannot have an effect (laggedoutcomes, etc)
Della Vigna and Kaplan (2007, QJE): effect of Fox News availabilityon Republican vote share
I Availability in 2000/2003 can’t affect past vote shares.
Unconfoundedness could still be violated even if you pass this test!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 42 / 176
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Assessing no unmeasured confounders
Can do “placebo” tests, where Di cannot have an effect (laggedoutcomes, etc)
Della Vigna and Kaplan (2007, QJE): effect of Fox News availabilityon Republican vote share
I Availability in 2000/2003 can’t affect past vote shares.
Unconfoundedness could still be violated even if you pass this test!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 42 / 176
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Assessing no unmeasured confounders
Can do “placebo” tests, where Di cannot have an effect (laggedoutcomes, etc)
Della Vigna and Kaplan (2007, QJE): effect of Fox News availabilityon Republican vote share
I Availability in 2000/2003 can’t affect past vote shares.
Unconfoundedness could still be violated even if you pass this test!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 42 / 176
![Page 159: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/159.jpg)
Assessing no unmeasured confounders
Can do “placebo” tests, where Di cannot have an effect (laggedoutcomes, etc)
Della Vigna and Kaplan (2007, QJE): effect of Fox News availabilityon Republican vote share
I Availability in 2000/2003 can’t affect past vote shares.
Unconfoundedness could still be violated even if you pass this test!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 42 / 176
![Page 160: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/160.jpg)
Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
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Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
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Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
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Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
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Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
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Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)
I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
![Page 166: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/166.jpg)
Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)
I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
![Page 167: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/167.jpg)
Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)
I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
![Page 168: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/168.jpg)
Alternatives to no unmeasured confounding
Without explicit randomization, we need some way of identifyingcausal effects.
No unmeasured confounders ≈ randomized experiment.
I Identification results very similar to experiments.
With unmeasured confounding are we doomed? Maybe not!
Other approaches rely on finding plausibly exogenous variation inassignment of Di :
I Instrumental variables (randomization + exclusion restriction)I Over-time variation (diff-in-diff, fixed effects)I Arbitrary thresholds for treatment assignment (RDD)I All discussed in the next couple of weeks!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 43 / 176
![Page 169: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/169.jpg)
Summary
Today we discussed issues of identification (with just a dash ofestimation via stratification)
Next class we will talk about estimation and what OLS is doing underthis framework.
Causal inference is hard but worth doing!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 44 / 176
![Page 170: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/170.jpg)
Summary
Today we discussed issues of identification (with just a dash ofestimation via stratification)
Next class we will talk about estimation and what OLS is doing underthis framework.
Causal inference is hard but worth doing!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 44 / 176
![Page 171: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/171.jpg)
Summary
Today we discussed issues of identification (with just a dash ofestimation via stratification)
Next class we will talk about estimation and what OLS is doing underthis framework.
Causal inference is hard but worth doing!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 44 / 176
![Page 172: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/172.jpg)
Fun with Censorship
Often you don’t need sophisticated methods to reveal interestingfindings
“Ansolabahere’s Law”: real relationship is visible in a bivariate plotand remains in a more sophisticated in a statistical model
In other words: all inferences require both visual and mathematicalevidence
Example: King, Pan and Roberts (2013) “How Censorship in ChinaAllows Government Criticism but Silences Collective Expression”American Political Science Review
They use very simple (statistical) methods to great effect.
This line of work is one of my favorites.
Sequence of slides that follow courtesy of King, Pan and Roberts
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 45 / 176
![Page 173: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/173.jpg)
Fun with Censorship
Often you don’t need sophisticated methods to reveal interestingfindings
“Ansolabahere’s Law”: real relationship is visible in a bivariate plotand remains in a more sophisticated in a statistical model
In other words: all inferences require both visual and mathematicalevidence
Example: King, Pan and Roberts (2013) “How Censorship in ChinaAllows Government Criticism but Silences Collective Expression”American Political Science Review
They use very simple (statistical) methods to great effect.
This line of work is one of my favorites.
Sequence of slides that follow courtesy of King, Pan and Roberts
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 45 / 176
![Page 174: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/174.jpg)
Fun with Censorship
Often you don’t need sophisticated methods to reveal interestingfindings
“Ansolabahere’s Law”: real relationship is visible in a bivariate plotand remains in a more sophisticated in a statistical model
In other words: all inferences require both visual and mathematicalevidence
Example: King, Pan and Roberts (2013) “How Censorship in ChinaAllows Government Criticism but Silences Collective Expression”American Political Science Review
They use very simple (statistical) methods to great effect.
This line of work is one of my favorites.
Sequence of slides that follow courtesy of King, Pan and Roberts
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 45 / 176
![Page 175: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/175.jpg)
Fun with Censorship
Often you don’t need sophisticated methods to reveal interestingfindings
“Ansolabahere’s Law”: real relationship is visible in a bivariate plotand remains in a more sophisticated in a statistical model
In other words: all inferences require both visual and mathematicalevidence
Example: King, Pan and Roberts (2013) “How Censorship in ChinaAllows Government Criticism but Silences Collective Expression”American Political Science Review
They use very simple (statistical) methods to great effect.
This line of work is one of my favorites.
Sequence of slides that follow courtesy of King, Pan and Roberts
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 45 / 176
![Page 176: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/176.jpg)
Fun with Censorship
Often you don’t need sophisticated methods to reveal interestingfindings
“Ansolabahere’s Law”: real relationship is visible in a bivariate plotand remains in a more sophisticated in a statistical model
In other words: all inferences require both visual and mathematicalevidence
Example: King, Pan and Roberts (2013) “How Censorship in ChinaAllows Government Criticism but Silences Collective Expression”American Political Science Review
They use very simple (statistical) methods to great effect.
This line of work is one of my favorites.
Sequence of slides that follow courtesy of King, Pan and Roberts
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 45 / 176
![Page 177: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/177.jpg)
Fun with Censorship
Often you don’t need sophisticated methods to reveal interestingfindings
“Ansolabahere’s Law”: real relationship is visible in a bivariate plotand remains in a more sophisticated in a statistical model
In other words: all inferences require both visual and mathematicalevidence
Example: King, Pan and Roberts (2013) “How Censorship in ChinaAllows Government Criticism but Silences Collective Expression”American Political Science Review
They use very simple (statistical) methods to great effect.
This line of work is one of my favorites.
Sequence of slides that follow courtesy of King, Pan and Roberts
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 45 / 176
![Page 178: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/178.jpg)
Fun with Censorship
Often you don’t need sophisticated methods to reveal interestingfindings
“Ansolabahere’s Law”: real relationship is visible in a bivariate plotand remains in a more sophisticated in a statistical model
In other words: all inferences require both visual and mathematicalevidence
Example: King, Pan and Roberts (2013) “How Censorship in ChinaAllows Government Criticism but Silences Collective Expression”American Political Science Review
They use very simple (statistical) methods to great effect.
This line of work is one of my favorites.
Sequence of slides that follow courtesy of King, Pan and Roberts
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 45 / 176
![Page 179: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/179.jpg)
Chinese Censorship
The largest selective suppression of human expression in history
:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop collective action
Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 180: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/180.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop collective action
Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 181: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/181.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop collective action
Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 182: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/182.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state
2 Stop collective action
Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 183: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/183.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state
2 Stop collective action
Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 184: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/184.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state
2 Stop collective action
Right
Either or both could be right or wrong.
(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 185: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/185.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action
Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 186: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/186.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 187: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/187.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 188: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/188.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 189: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/189.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 190: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/190.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 191: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/191.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 192: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/192.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 193: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/193.jpg)
Chinese Censorship
The largest selective suppression of human expression in history:
implemented manually,
by ≈ 200, 000 workers,
located in government and inside social media firms
Theories of the Goal of Censorship
1 Stop criticism of the state Wrong
2 Stop collective action Right
Either or both could be right or wrong.
(They also censor 2 other smaller categories)
Benefit
?
Huge
Cost
Huge
Small
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 46 / 176
![Page 194: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/194.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):
I Download content the instant it appearsI (Carefully) revisit each later to determine if it was censoredI Use computer-assisted methods of text analysis (some existing, some
new, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 195: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/195.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):
I Download content the instant it appearsI (Carefully) revisit each later to determine if it was censoredI Use computer-assisted methods of text analysis (some existing, some
new, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 196: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/196.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):
I Download content the instant it appearsI (Carefully) revisit each later to determine if it was censoredI Use computer-assisted methods of text analysis (some existing, some
new, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 197: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/197.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):
I Download content the instant it appearsI (Carefully) revisit each later to determine if it was censoredI Use computer-assisted methods of text analysis (some existing, some
new, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 198: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/198.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):
I Download content the instant it appearsI (Carefully) revisit each later to determine if it was censoredI Use computer-assisted methods of text analysis (some existing, some
new, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 199: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/199.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):I Download content the instant it appears
I (Carefully) revisit each later to determine if it was censoredI Use computer-assisted methods of text analysis (some existing, some
new, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 200: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/200.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):I Download content the instant it appearsI (Carefully) revisit each later to determine if it was censored
I Use computer-assisted methods of text analysis (some existing, somenew, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 201: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/201.jpg)
Observational Study
Collect 3,674,698 social media posts in 85 topic areas over 6 months
Random sample: 127,283
(Repeat design; Total analyzed: 11,382,221)
For each post (on a timeline in one of 85 content areas):I Download content the instant it appearsI (Carefully) revisit each later to determine if it was censoredI Use computer-assisted methods of text analysis (some existing, some
new, all adapted to Chinese)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 47 / 176
![Page 202: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/202.jpg)
Censorship is not Ambiguous: BBS Error Page
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 48 / 176
![Page 203: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/203.jpg)
For 2 Unusual Topics: Constant Censorship Effort
05
1015
Count
Jan Mar Apr May Jun
Count PublishedCount Censored
Count PublishedCount Censored
010
2030
4050
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored
Students Throw Shoes at
Fang BinXing
Pornography Criticism of the Censors
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 49 / 176
![Page 204: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/204.jpg)
For 2 Unusual Topics: Constant Censorship Effort0
510
15
Count
Jan Mar Apr May Jun
Count PublishedCount Censored
Count PublishedCount Censored
010
2030
4050
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored
Students Throw Shoes at
Fang BinXing
Pornography Criticism of the Censors
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 49 / 176
![Page 205: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/205.jpg)
All other topics: Censorship & Post Volume are “Bursty”
010
2030
4050
6070
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:
I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 206: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/206.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:
I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 207: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/207.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:
I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 208: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/208.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burst
I (≈ 3 SDs greaterthan baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 209: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/209.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 210: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/210.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 211: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/211.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 212: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/212.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 213: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/213.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 214: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/214.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 215: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/215.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 216: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/216.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 217: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/217.jpg)
All other topics: Censorship & Post Volume are “Bursty”0
1020
3040
5060
70
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored Riots in
Zengcheng
Unit of analysis:I volume burstI (≈ 3 SDs greater
than baseline volume)
They monitored 85 topicareas (Jan–July 2011)
Found 87 volume burstsin total
Identified real worldevents associated witheach burst
Their hypothesis: The government censors all posts in volume burstsassociated with events with collective action (regardless of how critical orsupportive of the state)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 50 / 176
![Page 218: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/218.jpg)
Observational Test 1: Post Volume
Begin with 87 volume bursts in 85 topics areas
For each burst, calculate change in % censorship inside to outsideeach volume burst within topic areas – censorship magnitude
If goal of censorship is to stop collective action, they expect:
1 On average, % censoredshould increase duringvolume bursts
2 Some bursts (associatedwith politically relevantevents) should havemuch higher censorship -0.2 0.0 0.2 0.4 0.6 0.8
01
23
45
Censorship Magnitude
Density
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 51 / 176
![Page 219: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/219.jpg)
Observational Test 1: Post Volume
Begin with 87 volume bursts in 85 topics areas
For each burst, calculate change in % censorship inside to outsideeach volume burst within topic areas – censorship magnitude
If goal of censorship is to stop collective action, they expect:
1 On average, % censoredshould increase duringvolume bursts
2 Some bursts (associatedwith politically relevantevents) should havemuch higher censorship -0.2 0.0 0.2 0.4 0.6 0.8
01
23
45
Censorship Magnitude
Density
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 51 / 176
![Page 220: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/220.jpg)
Observational Test 1: Post Volume
Begin with 87 volume bursts in 85 topics areas
For each burst, calculate change in % censorship inside to outsideeach volume burst within topic areas – censorship magnitude
If goal of censorship is to stop collective action, they expect:
1 On average, % censoredshould increase duringvolume bursts
2 Some bursts (associatedwith politically relevantevents) should havemuch higher censorship -0.2 0.0 0.2 0.4 0.6 0.8
01
23
45
Censorship Magnitude
Density
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 51 / 176
![Page 221: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/221.jpg)
Observational Test 1: Post Volume
Begin with 87 volume bursts in 85 topics areas
For each burst, calculate change in % censorship inside to outsideeach volume burst within topic areas – censorship magnitude
If goal of censorship is to stop collective action, they expect:
1 On average, % censoredshould increase duringvolume bursts
2 Some bursts (associatedwith politically relevantevents) should havemuch higher censorship -0.2 0.0 0.2 0.4 0.6 0.8
01
23
45
Censorship Magnitude
Density
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 51 / 176
![Page 222: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/222.jpg)
Observational Test 1: Post Volume
Begin with 87 volume bursts in 85 topics areas
For each burst, calculate change in % censorship inside to outsideeach volume burst within topic areas – censorship magnitude
If goal of censorship is to stop collective action, they expect:
1 On average, % censoredshould increase duringvolume bursts
2 Some bursts (associatedwith politically relevantevents) should havemuch higher censorship -0.2 0.0 0.2 0.4 0.6 0.8
01
23
45
Censorship Magnitude
Density
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 51 / 176
![Page 223: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/223.jpg)
Observational Test 1: Post Volume
Begin with 87 volume bursts in 85 topics areas
For each burst, calculate change in % censorship inside to outsideeach volume burst within topic areas – censorship magnitude
If goal of censorship is to stop collective action, they expect:
1 On average, % censoredshould increase duringvolume bursts
2 Some bursts (associatedwith politically relevantevents) should havemuch higher censorship
-0.2 0.0 0.2 0.4 0.6 0.8
01
23
45
Censorship Magnitude
Density
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 51 / 176
![Page 224: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/224.jpg)
Observational Test 1: Post Volume
Begin with 87 volume bursts in 85 topics areas
For each burst, calculate change in % censorship inside to outsideeach volume burst within topic areas – censorship magnitude
If goal of censorship is to stop collective action, they expect:
1 On average, % censoredshould increase duringvolume bursts
2 Some bursts (associatedwith politically relevantevents) should havemuch higher censorship -0.2 0.0 0.2 0.4 0.6 0.8
01
23
45
Censorship Magnitude
Density
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 51 / 176
![Page 225: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/225.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1
I protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2
3
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 226: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/226.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1
I protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2
3
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 227: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/227.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action Potential
I protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2
3
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 228: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/228.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the Internet
I individuals who have organized or incited collective action on theground in the past;
I topics related to nationalism or nationalist sentiment that have incitedprotest or collective action in the past.
2
3
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 229: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/229.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;
I topics related to nationalism or nationalist sentiment that have incitedprotest or collective action in the past.
2
3
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 230: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/230.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2
3
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 231: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/231.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 232: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/232.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 233: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/233.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 234: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/234.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
![Page 235: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/235.jpg)
Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
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Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
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Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
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Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
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Observational Test 2: The Event Generating VolumeBursts
Event classification (each category can be +, −, or neutral commentsabout the state)
1 Collective Action PotentialI protest or organized crowd formation outside the InternetI individuals who have organized or incited collective action on the
ground in the past;I topics related to nationalism or nationalist sentiment that have incited
protest or collective action in the past.
2 Criticism of censors
3 Pornography
4 (Other) News
5 Government Policies
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 52 / 176
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What Types of Events Are Censored?
Censorship Magnitude
Density
02
46
810
12
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Collective ActionCriticism of CensorsPornography
PolicyNews
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What Types of Events Are Censored?
Censorship Magnitude
Popular Book Published in Audio FormatDisney Announced Theme ParkEPA Issues New Rules on LeadChinese Solar Company Announces EarningsChina Puts Nuclear Program on HoldGov't Increases Power PricesJon Hunstman Steps Down as Ambassador to ChinaNews About Iran Nuclear ProgramIndoor Smoking Ban Takes EffectPopular Video Game ReleasedEducation Reform for Migrant ChildrenFood Prices RiseU.S. Military Intervention in Libya
Censorship Magnitude
New Laws on Fifty Cent PartyRush to Buy Salt After Earthquake
Students Throw Shoes at Fang BinXingFuzhou Bombing
Localized Advocacy for Environment LotteryGoogle is Hacked
Collective Anger At Lead Poisoning in JiangsuAi Weiwei Arrested
Pornography Mentioning Popular BookZengcheng Protests
Baidu Copyright LawsuitPornography Disguised as News
Protests in Inner Mongolia
PoliciesNews
Collective ActionCriticism of CensorsPornography
-0.2 0 0.1 0.3 0.5 0.7
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Censoring Collective Action: Ai Weiwei’s Arrest
010
2030
40
Count
Jan Mar Apr May Jun Jul
Count PublishedCount Censored
Ai Weiwei arrested
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Censoring Collective Action: Protests in Inner Mongolia
05
1015
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored
Protests in Inner Mongolia
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 55 / 176
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Low Censorship on One Child Policy
010
2030
40
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored
Speculation of
Policy Reversal at NPC
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 56 / 176
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Low Censorship on News: Power Prices
010
2030
4050
6070
Count
Jan Feb Mar Apr May Jun Jul
Count PublishedCount Censored
Power shortages Gov't raises power prices
to curb demand
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Where We’ve Been and Where We’re Going...
Last WeekI regression diagnostics
This WeekI Monday:
F experimental IdealF identification with measured confounding
I Wednesday:F regression estimation
Next WeekI identification with unmeasured confoundingI instrumental variables
Long RunI causality with measured confounding → unmeasured confounding →
repeated data
Questions?
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Regression
David Freedman:
I sometimes have a nightmare about Kepler. Suppose a few of uswere transported back in time to the year 1600, and were invitedby the Emperor Rudolph II to set up an Imperial Department ofStatistics in the court at Prague. Despairing of those circularorbits, Kepler enrolls in our department. We teach him thegeneral linear model, least squares, dummy variables, everything.He goes back to work, fits the best circular orbit for Mars byleast squares, puts in a dummy variable for the exceptionalobservation - and publishes. And that’s the end, right there inPrague at the beginning of the 17th century.
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Regression and Causality
Regression is an estimation strategy that can be used with anidentification strategy to estimate a causal effect
When is regression causal? When the CEF is causal.
This means that the question of whether regression has a causalinterpretation is a question about identification
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Regression and Causality
Regression is an estimation strategy that can be used with anidentification strategy to estimate a causal effect
When is regression causal? When the CEF is causal.
This means that the question of whether regression has a causalinterpretation is a question about identification
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 60 / 176
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Regression and Causality
Regression is an estimation strategy that can be used with anidentification strategy to estimate a causal effect
When is regression causal?
When the CEF is causal.
This means that the question of whether regression has a causalinterpretation is a question about identification
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 60 / 176
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Regression and Causality
Regression is an estimation strategy that can be used with anidentification strategy to estimate a causal effect
When is regression causal? When the CEF is causal.
This means that the question of whether regression has a causalinterpretation is a question about identification
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 60 / 176
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Regression and Causality
Regression is an estimation strategy that can be used with anidentification strategy to estimate a causal effect
When is regression causal? When the CEF is causal.
This means that the question of whether regression has a causalinterpretation is a question about identification
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 60 / 176
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Identification under Selection on Observables: Regression
Consider the linear regression of Yi = β0 + τDi + X ′i β + εi .
Given selection on observables, there are mainly three identificationscenarios:
1 Constant treatment effects and outcomes are linear in X
I τ will provide unbiased and consistent estimates of ATE.
2 Constant treatment effects and unknown functional form
I τ will provide well-defined linear approximation to the average causalresponse function E[Y |D = 1,X ]− E[Y |D = 0,X ]. Approximationmay be very poor if E[Y |D,X ] is misspecified and then τ may bebiased for the ATE.
3 Heterogeneous treatment effects (τ differs for different values of X )
I If outcomes are linear in X , τ is unbiased and consistent estimator forconditional-variance-weighted average of the underlying causal effects.This average is often different from the ATE.
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Identification under Selection on Observables: Regression
Consider the linear regression of Yi = β0 + τDi + X ′i β + εi .
Given selection on observables, there are mainly three identificationscenarios:
1 Constant treatment effects and outcomes are linear in X
I τ will provide unbiased and consistent estimates of ATE.
2 Constant treatment effects and unknown functional form
I τ will provide well-defined linear approximation to the average causalresponse function E[Y |D = 1,X ]− E[Y |D = 0,X ]. Approximationmay be very poor if E[Y |D,X ] is misspecified and then τ may bebiased for the ATE.
3 Heterogeneous treatment effects (τ differs for different values of X )
I If outcomes are linear in X , τ is unbiased and consistent estimator forconditional-variance-weighted average of the underlying causal effects.This average is often different from the ATE.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 61 / 176
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Identification under Selection on Observables: Regression
Consider the linear regression of Yi = β0 + τDi + X ′i β + εi .
Given selection on observables, there are mainly three identificationscenarios:
1 Constant treatment effects and outcomes are linear in X
I τ will provide unbiased and consistent estimates of ATE.
2 Constant treatment effects and unknown functional form
I τ will provide well-defined linear approximation to the average causalresponse function E[Y |D = 1,X ]− E[Y |D = 0,X ]. Approximationmay be very poor if E[Y |D,X ] is misspecified and then τ may bebiased for the ATE.
3 Heterogeneous treatment effects (τ differs for different values of X )
I If outcomes are linear in X , τ is unbiased and consistent estimator forconditional-variance-weighted average of the underlying causal effects.This average is often different from the ATE.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 61 / 176
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Identification under Selection on Observables: Regression
Consider the linear regression of Yi = β0 + τDi + X ′i β + εi .
Given selection on observables, there are mainly three identificationscenarios:
1 Constant treatment effects and outcomes are linear in X
I τ will provide unbiased and consistent estimates of ATE.
2 Constant treatment effects and unknown functional form
I τ will provide well-defined linear approximation to the average causalresponse function E[Y |D = 1,X ]− E[Y |D = 0,X ]. Approximationmay be very poor if E[Y |D,X ] is misspecified and then τ may bebiased for the ATE.
3 Heterogeneous treatment effects (τ differs for different values of X )
I If outcomes are linear in X , τ is unbiased and consistent estimator forconditional-variance-weighted average of the underlying causal effects.This average is often different from the ATE.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 61 / 176
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Identification under Selection on Observables: Regression
Identification Assumption
1 Constant treatment effect: τ = Y1i − Y0i for all i
2 Control outcome is linear in X : Y0i = β0 + X ′i β + εi with εi⊥⊥Xi (noomitted variables and linearly separable confounding)
Identification Result
Then τATE = E[Y1 − Y0] is identified by a regression of the observedoutcome on the covariates and the treatment indicatorYi = β0 + τDi + X ′i β + εi
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Ideal Case: Linear Constant Effects ModelAssume constant linear effects and linearly separable confounding:
Yi (d) = Yi = β0 + τDi + ηi
Linearly separable confounding: assume that E[ηi |Xi ] = X ′i β,which means that ηi = X ′i β + εi where E[εi |Xi ] = 0.
Under this model, (Y1,Y0)⊥⊥D|X implies εi |X⊥⊥DAs a result,
Yi = β0 + τDi + E[ηi ]
= β0 + τDi + X ′i β + E[εi ]
= β0 + τDi + X ′i β
Thus, a regression where Di and Xi are entered linearly can recoverthe ATE.
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Ideal Case: Linear Constant Effects ModelAssume constant linear effects and linearly separable confounding:
Yi (d) = Yi = β0 + τDi + ηi
Linearly separable confounding: assume that E[ηi |Xi ] = X ′i β,which means that ηi = X ′i β + εi where E[εi |Xi ] = 0.
Under this model, (Y1,Y0)⊥⊥D|X implies εi |X⊥⊥DAs a result,
Yi = β0 + τDi + E[ηi ]
= β0 + τDi + X ′i β + E[εi ]
= β0 + τDi + X ′i β
Thus, a regression where Di and Xi are entered linearly can recoverthe ATE.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 63 / 176
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Ideal Case: Linear Constant Effects ModelAssume constant linear effects and linearly separable confounding:
Yi (d) = Yi = β0 + τDi + ηi
Linearly separable confounding: assume that E[ηi |Xi ] = X ′i β,which means that ηi = X ′i β + εi where E[εi |Xi ] = 0.
Under this model, (Y1,Y0)⊥⊥D|X implies εi |X⊥⊥D
As a result,
Yi = β0 + τDi + E[ηi ]
= β0 + τDi + X ′i β + E[εi ]
= β0 + τDi + X ′i β
Thus, a regression where Di and Xi are entered linearly can recoverthe ATE.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 63 / 176
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Ideal Case: Linear Constant Effects ModelAssume constant linear effects and linearly separable confounding:
Yi (d) = Yi = β0 + τDi + ηi
Linearly separable confounding: assume that E[ηi |Xi ] = X ′i β,which means that ηi = X ′i β + εi where E[εi |Xi ] = 0.
Under this model, (Y1,Y0)⊥⊥D|X implies εi |X⊥⊥DAs a result,
Yi = β0 + τDi + E[ηi ]
= β0 + τDi + X ′i β + E[εi ]
= β0 + τDi + X ′i β
Thus, a regression where Di and Xi are entered linearly can recoverthe ATE.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 63 / 176
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Ideal Case: Linear Constant Effects ModelAssume constant linear effects and linearly separable confounding:
Yi (d) = Yi = β0 + τDi + ηi
Linearly separable confounding: assume that E[ηi |Xi ] = X ′i β,which means that ηi = X ′i β + εi where E[εi |Xi ] = 0.
Under this model, (Y1,Y0)⊥⊥D|X implies εi |X⊥⊥DAs a result,
Yi = β0 + τDi + E[ηi ]
= β0 + τDi + X ′i β + E[εi ]
= β0 + τDi + X ′i β
Thus, a regression where Di and Xi are entered linearly can recoverthe ATE.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 63 / 176
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Implausible Plausible
Constant effects and linearly separable confounding aren’t veryappealing or plausible assumptions
To understand what happens when they don’t hold, we need tounderstand the properties of regression with minimal assumptions:this is often called an agnostic view of regression.
The Aronow and Miller book is an excellent introduction to theagnostic view of regression and I recommend checking it out. Here Iwill give you just a flavor of it.
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Implausible Plausible
Constant effects and linearly separable confounding aren’t veryappealing or plausible assumptions
To understand what happens when they don’t hold, we need tounderstand the properties of regression with minimal assumptions:this is often called an agnostic view of regression.
The Aronow and Miller book is an excellent introduction to theagnostic view of regression and I recommend checking it out. Here Iwill give you just a flavor of it.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 64 / 176
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Implausible Plausible
Constant effects and linearly separable confounding aren’t veryappealing or plausible assumptions
To understand what happens when they don’t hold, we need tounderstand the properties of regression with minimal assumptions:this is often called an agnostic view of regression.
The Aronow and Miller book is an excellent introduction to theagnostic view of regression and I recommend checking it out. Here Iwill give you just a flavor of it.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 64 / 176
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Implausible Plausible
Constant effects and linearly separable confounding aren’t veryappealing or plausible assumptions
To understand what happens when they don’t hold, we need tounderstand the properties of regression with minimal assumptions:this is often called an agnostic view of regression.
The Aronow and Miller book is an excellent introduction to theagnostic view of regression and I recommend checking it out. Here Iwill give you just a flavor of it.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 64 / 176
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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Regression as parametric modeling
Let’s start with the parametric view we have taken thus far.
Gauss-Markov assumptions:
I linearity, i.i.d. sample, full rank Xi , zero conditional mean error,homoskedasticity.
OLS is BLUE, plus normality of the errors and we get small sampleSEs.
What is the basic approach here? It is a model for the conditionaldistribution of Yi given Xi :
[Yi |Xi ] ∼ N(X ′i β, σ2)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 66 / 176
![Page 270: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/270.jpg)
Regression as parametric modeling
Let’s start with the parametric view we have taken thus far.
Gauss-Markov assumptions:
I linearity, i.i.d. sample, full rank Xi , zero conditional mean error,homoskedasticity.
OLS is BLUE, plus normality of the errors and we get small sampleSEs.
What is the basic approach here? It is a model for the conditionaldistribution of Yi given Xi :
[Yi |Xi ] ∼ N(X ′i β, σ2)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 66 / 176
![Page 271: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/271.jpg)
Regression as parametric modeling
Let’s start with the parametric view we have taken thus far.
Gauss-Markov assumptions:
I linearity, i.i.d. sample, full rank Xi , zero conditional mean error,homoskedasticity.
OLS is BLUE, plus normality of the errors and we get small sampleSEs.
What is the basic approach here? It is a model for the conditionaldistribution of Yi given Xi :
[Yi |Xi ] ∼ N(X ′i β, σ2)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 66 / 176
![Page 272: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/272.jpg)
Regression as parametric modeling
Let’s start with the parametric view we have taken thus far.
Gauss-Markov assumptions:
I linearity, i.i.d. sample, full rank Xi , zero conditional mean error,homoskedasticity.
OLS is BLUE, plus normality of the errors and we get small sampleSEs.
What is the basic approach here? It is a model for the conditionaldistribution of Yi given Xi :
[Yi |Xi ] ∼ N(X ′i β, σ2)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 66 / 176
![Page 273: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/273.jpg)
Agnostic views on regression
[Yi |Xi ] ∼ N(X ′i β, σ2)
Above parametric view has strong distributional assumption on Yi .
Properties like BLUE or BUE depend on these assumptions holding.
Alternative: take an agnostic view on regression.
I Use OLS without believing these assumptions.
Lose the distributional assumptions, focus on the conditionalexpectation function (CEF):
µ(x) = E[Yi |Xi = x ] =∑y
y · P[Yi = y |Xi = x ]
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 67 / 176
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Agnostic views on regression
[Yi |Xi ] ∼ N(X ′i β, σ2)
Above parametric view has strong distributional assumption on Yi .
Properties like BLUE or BUE depend on these assumptions holding.
Alternative: take an agnostic view on regression.
I Use OLS without believing these assumptions.
Lose the distributional assumptions, focus on the conditionalexpectation function (CEF):
µ(x) = E[Yi |Xi = x ] =∑y
y · P[Yi = y |Xi = x ]
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 67 / 176
![Page 275: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/275.jpg)
Agnostic views on regression
[Yi |Xi ] ∼ N(X ′i β, σ2)
Above parametric view has strong distributional assumption on Yi .
Properties like BLUE or BUE depend on these assumptions holding.
Alternative: take an agnostic view on regression.
I Use OLS without believing these assumptions.
Lose the distributional assumptions, focus on the conditionalexpectation function (CEF):
µ(x) = E[Yi |Xi = x ] =∑y
y · P[Yi = y |Xi = x ]
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 67 / 176
![Page 276: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/276.jpg)
Agnostic views on regression
[Yi |Xi ] ∼ N(X ′i β, σ2)
Above parametric view has strong distributional assumption on Yi .
Properties like BLUE or BUE depend on these assumptions holding.
Alternative: take an agnostic view on regression.
I Use OLS without believing these assumptions.
Lose the distributional assumptions, focus on the conditionalexpectation function (CEF):
µ(x) = E[Yi |Xi = x ] =∑y
y · P[Yi = y |Xi = x ]
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 67 / 176
![Page 277: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/277.jpg)
Agnostic views on regression
[Yi |Xi ] ∼ N(X ′i β, σ2)
Above parametric view has strong distributional assumption on Yi .
Properties like BLUE or BUE depend on these assumptions holding.
Alternative: take an agnostic view on regression.
I Use OLS without believing these assumptions.
Lose the distributional assumptions, focus on the conditionalexpectation function (CEF):
µ(x) = E[Yi |Xi = x ] =∑y
y · P[Yi = y |Xi = x ]
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 67 / 176
![Page 278: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/278.jpg)
Justifying linear regression
Define linear regression:
β = arg minb
E[(Yi − X ′i b)2]
The solution to this is the following:
β = E[XiX′i ]−1E[XiYi ]
Note that the is the population coefficient vector, not the estimatoryet.
In other words, even a non-linear CEF has a “true” linearapproximation, even though that approximation may not be great.
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Justifying linear regression
Define linear regression:
β = arg minb
E[(Yi − X ′i b)2]
The solution to this is the following:
β = E[XiX′i ]−1E[XiYi ]
Note that the is the population coefficient vector, not the estimatoryet.
In other words, even a non-linear CEF has a “true” linearapproximation, even though that approximation may not be great.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 68 / 176
![Page 280: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/280.jpg)
Justifying linear regression
Define linear regression:
β = arg minb
E[(Yi − X ′i b)2]
The solution to this is the following:
β = E[XiX′i ]−1E[XiYi ]
Note that the is the population coefficient vector, not the estimatoryet.
In other words, even a non-linear CEF has a “true” linearapproximation, even though that approximation may not be great.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 68 / 176
![Page 281: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/281.jpg)
Justifying linear regression
Define linear regression:
β = arg minb
E[(Yi − X ′i b)2]
The solution to this is the following:
β = E[XiX′i ]−1E[XiYi ]
Note that the is the population coefficient vector, not the estimatoryet.
In other words, even a non-linear CEF has a “true” linearapproximation, even though that approximation may not be great.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 68 / 176
![Page 282: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/282.jpg)
Regression anatomy
Consider simple linear regression:
(α, β) = arg mina,b
E[(Yi − a− bXi )
2]
In this case, we can write the population/true slope β as:
β = E[XiX′i ]−1E[XiYi ] =
Cov(Yi ,Xi )
Var[Xi ]
With more covariates, β is more complicated, but we can still write itlike this.
Let Xki be the residual from a regression of Xki on all the otherindependent variables. Then, βk , the coefficient for Xki is:
βk =Cov(Yi , Xki )
Var(Xki )
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Regression anatomy
Consider simple linear regression:
(α, β) = arg mina,b
E[(Yi − a− bXi )
2]
In this case, we can write the population/true slope β as:
β = E[XiX′i ]−1E[XiYi ] =
Cov(Yi ,Xi )
Var[Xi ]
With more covariates, β is more complicated, but we can still write itlike this.
Let Xki be the residual from a regression of Xki on all the otherindependent variables. Then, βk , the coefficient for Xki is:
βk =Cov(Yi , Xki )
Var(Xki )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 69 / 176
![Page 284: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/284.jpg)
Regression anatomy
Consider simple linear regression:
(α, β) = arg mina,b
E[(Yi − a− bXi )
2]
In this case, we can write the population/true slope β as:
β = E[XiX′i ]−1E[XiYi ] =
Cov(Yi ,Xi )
Var[Xi ]
With more covariates, β is more complicated, but we can still write itlike this.
Let Xki be the residual from a regression of Xki on all the otherindependent variables. Then, βk , the coefficient for Xki is:
βk =Cov(Yi , Xki )
Var(Xki )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 69 / 176
![Page 285: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/285.jpg)
Regression anatomy
Consider simple linear regression:
(α, β) = arg mina,b
E[(Yi − a− bXi )
2]
In this case, we can write the population/true slope β as:
β = E[XiX′i ]−1E[XiYi ] =
Cov(Yi ,Xi )
Var[Xi ]
With more covariates, β is more complicated, but we can still write itlike this.
Let Xki be the residual from a regression of Xki on all the otherindependent variables. Then, βk , the coefficient for Xki is:
βk =Cov(Yi , Xki )
Var(Xki )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 69 / 176
![Page 286: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/286.jpg)
Justification 1: Linear CEFs
Justification 1: if the CEF is linear, the population regression functionis it. That is, if E [Yi |Xi ] = X ′i b, then b = β.
When would we expect the CEF to be linear? Two cases.
1 Outcome and covariates are multivariate normal.2 Linear regression model is saturated.
A model is saturated if there are as many parameters as there arepossible combination of the Xi variables.
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Justification 1: Linear CEFs
Justification 1: if the CEF is linear, the population regression functionis it. That is, if E [Yi |Xi ] = X ′i b, then b = β.
When would we expect the CEF to be linear? Two cases.
1 Outcome and covariates are multivariate normal.2 Linear regression model is saturated.
A model is saturated if there are as many parameters as there arepossible combination of the Xi variables.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 70 / 176
![Page 288: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/288.jpg)
Justification 1: Linear CEFs
Justification 1: if the CEF is linear, the population regression functionis it. That is, if E [Yi |Xi ] = X ′i b, then b = β.
When would we expect the CEF to be linear? Two cases.
1 Outcome and covariates are multivariate normal.2 Linear regression model is saturated.
A model is saturated if there are as many parameters as there arepossible combination of the Xi variables.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 70 / 176
![Page 289: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/289.jpg)
Justification 1: Linear CEFs
Justification 1: if the CEF is linear, the population regression functionis it. That is, if E [Yi |Xi ] = X ′i b, then b = β.
When would we expect the CEF to be linear? Two cases.
1 Outcome and covariates are multivariate normal.2 Linear regression model is saturated.
A model is saturated if there are as many parameters as there arepossible combination of the Xi variables.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 70 / 176
![Page 290: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/290.jpg)
Justification 1: Linear CEFs
Justification 1: if the CEF is linear, the population regression functionis it. That is, if E [Yi |Xi ] = X ′i b, then b = β.
When would we expect the CEF to be linear? Two cases.
1 Outcome and covariates are multivariate normal.2 Linear regression model is saturated.
A model is saturated if there are as many parameters as there arepossible combination of the Xi variables.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 70 / 176
![Page 291: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/291.jpg)
Saturated model example
Two binary variables, X1i for marriage status and X2i for havingchildren.
Four possible values of Xi , four possible values of µ(Xi ):
E [Yi |X1i = 0,X2i = 0]
E [Yi |X1i = 1,X2i = 0]
E [Yi |X1i = 0,X2i = 1]
E [Yi |X1i = 1,X2i = 1]
We can write the CEF as follows:
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
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Saturated model example
Two binary variables, X1i for marriage status and X2i for havingchildren.
Four possible values of Xi , four possible values of µ(Xi ):
E [Yi |X1i = 0,X2i = 0]
E [Yi |X1i = 1,X2i = 0]
E [Yi |X1i = 0,X2i = 1]
E [Yi |X1i = 1,X2i = 1]
We can write the CEF as follows:
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 71 / 176
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Saturated model example
Two binary variables, X1i for marriage status and X2i for havingchildren.
Four possible values of Xi , four possible values of µ(Xi ):
E [Yi |X1i = 0,X2i = 0]
E [Yi |X1i = 1,X2i = 0]
E [Yi |X1i = 0,X2i = 1]
E [Yi |X1i = 1,X2i = 1]
We can write the CEF as follows:
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 71 / 176
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Saturated model example
Two binary variables, X1i for marriage status and X2i for havingchildren.
Four possible values of Xi , four possible values of µ(Xi ):
E [Yi |X1i = 0,X2i = 0] = α
E [Yi |X1i = 1,X2i = 0]
E [Yi |X1i = 0,X2i = 1]
E [Yi |X1i = 1,X2i = 1]
We can write the CEF as follows:
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 71 / 176
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Saturated model example
Two binary variables, X1i for marriage status and X2i for havingchildren.
Four possible values of Xi , four possible values of µ(Xi ):
E [Yi |X1i = 0,X2i = 0] = α
E [Yi |X1i = 1,X2i = 0] = α + β
E [Yi |X1i = 0,X2i = 1]
E [Yi |X1i = 1,X2i = 1]
We can write the CEF as follows:
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 71 / 176
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Saturated model example
Two binary variables, X1i for marriage status and X2i for havingchildren.
Four possible values of Xi , four possible values of µ(Xi ):
E [Yi |X1i = 0,X2i = 0] = α
E [Yi |X1i = 1,X2i = 0] = α + β
E [Yi |X1i = 0,X2i = 1] = α + γ
E [Yi |X1i = 1,X2i = 1]
We can write the CEF as follows:
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 71 / 176
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Saturated model example
Two binary variables, X1i for marriage status and X2i for havingchildren.
Four possible values of Xi , four possible values of µ(Xi ):
E [Yi |X1i = 0,X2i = 0] = α
E [Yi |X1i = 1,X2i = 0] = α + β
E [Yi |X1i = 0,X2i = 1] = α + γ
E [Yi |X1i = 1,X2i = 1] = α + β + γ + δ
We can write the CEF as follows:
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 71 / 176
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Saturated models example
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Basically, each value of µ(Xi ) is being estimated separately.
I within-strata estimation.I No borrowing of information from across values of Xi .
Requires a set of dummies for each categorical variable plus allinteractions.
Or, a series of dummies for each unique combination of Xi .
This makes linearity hold mechanically and so linearity is not anassumption.
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Saturated models example
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Basically, each value of µ(Xi ) is being estimated separately.
I within-strata estimation.
I No borrowing of information from across values of Xi .
Requires a set of dummies for each categorical variable plus allinteractions.
Or, a series of dummies for each unique combination of Xi .
This makes linearity hold mechanically and so linearity is not anassumption.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 72 / 176
![Page 300: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/300.jpg)
Saturated models example
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Basically, each value of µ(Xi ) is being estimated separately.
I within-strata estimation.I No borrowing of information from across values of Xi .
Requires a set of dummies for each categorical variable plus allinteractions.
Or, a series of dummies for each unique combination of Xi .
This makes linearity hold mechanically and so linearity is not anassumption.
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Saturated models example
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Basically, each value of µ(Xi ) is being estimated separately.
I within-strata estimation.I No borrowing of information from across values of Xi .
Requires a set of dummies for each categorical variable plus allinteractions.
Or, a series of dummies for each unique combination of Xi .
This makes linearity hold mechanically and so linearity is not anassumption.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 72 / 176
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Saturated models example
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Basically, each value of µ(Xi ) is being estimated separately.
I within-strata estimation.I No borrowing of information from across values of Xi .
Requires a set of dummies for each categorical variable plus allinteractions.
Or, a series of dummies for each unique combination of Xi .
This makes linearity hold mechanically and so linearity is not anassumption.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 72 / 176
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Saturated models example
E [Yi |X1i ,X2i ] = α + βX1i + γX2i + δ(X1iX2i )
Basically, each value of µ(Xi ) is being estimated separately.
I within-strata estimation.I No borrowing of information from across values of Xi .
Requires a set of dummies for each categorical variable plus allinteractions.
Or, a series of dummies for each unique combination of Xi .
This makes linearity hold mechanically and so linearity is not anassumption.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 72 / 176
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Saturated model example
Washington (AER) data on the effects of daughters.
We’ll look at the relationship between voting and number of kids(causal?).
girls <- foreign::read.dta("girls.dta")
head(girls[, c("name", "totchi", "aauw")])
## name totchi aauw
## 1 ABERCROMBIE, NEIL 0 100
## 2 ACKERMAN, GARY L. 3 88
## 3 ADERHOLT, ROBERT B. 0 0
## 4 ALLEN, THOMAS H. 2 100
## 5 ANDREWS, ROBERT E. 2 100
## 6 ARCHER, W.R. 7 0
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Saturated model example
Washington (AER) data on the effects of daughters.
We’ll look at the relationship between voting and number of kids(causal?).
girls <- foreign::read.dta("girls.dta")
head(girls[, c("name", "totchi", "aauw")])
## name totchi aauw
## 1 ABERCROMBIE, NEIL 0 100
## 2 ACKERMAN, GARY L. 3 88
## 3 ADERHOLT, ROBERT B. 0 0
## 4 ALLEN, THOMAS H. 2 100
## 5 ANDREWS, ROBERT E. 2 100
## 6 ARCHER, W.R. 7 0
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 73 / 176
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Saturated model example
Washington (AER) data on the effects of daughters.
We’ll look at the relationship between voting and number of kids(causal?).
girls <- foreign::read.dta("girls.dta")
head(girls[, c("name", "totchi", "aauw")])
## name totchi aauw
## 1 ABERCROMBIE, NEIL 0 100
## 2 ACKERMAN, GARY L. 3 88
## 3 ADERHOLT, ROBERT B. 0 0
## 4 ALLEN, THOMAS H. 2 100
## 5 ANDREWS, ROBERT E. 2 100
## 6 ARCHER, W.R. 7 0
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 73 / 176
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Linear model
summary(lm(aauw ~ totchi, data = girls))
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 61.31 1.81 33.81 <2e-16 ***
## totchi -5.33 0.62 -8.59 <2e-16 ***
## ---
## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1
##
## Residual standard error: 42 on 1733 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.0408, Adjusted R-squared: 0.0403
## F-statistic: 73.8 on 1 and 1733 DF, p-value: <2e-16
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Saturated modelsummary(lm(aauw ~ as.factor(totchi), data = girls))
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56.41 2.76 20.42 < 2e-16 ***
## as.factor(totchi)1 5.45 4.11 1.33 0.1851
## as.factor(totchi)2 -3.80 3.27 -1.16 0.2454
## as.factor(totchi)3 -13.65 3.45 -3.95 8.1e-05 ***
## as.factor(totchi)4 -19.31 4.01 -4.82 1.6e-06 ***
## as.factor(totchi)5 -15.46 4.85 -3.19 0.0015 **
## as.factor(totchi)6 -33.59 10.42 -3.22 0.0013 **
## as.factor(totchi)7 -17.13 11.41 -1.50 0.1336
## as.factor(totchi)8 -55.33 12.28 -4.51 7.0e-06 ***
## as.factor(totchi)9 -50.41 24.08 -2.09 0.0364 *
## as.factor(totchi)10 -53.41 20.90 -2.56 0.0107 *
## as.factor(totchi)12 -56.41 41.53 -1.36 0.1745
## ---
## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1
##
## Residual standard error: 41 on 1723 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.0506, Adjusted R-squared: 0.0446
## F-statistic: 8.36 on 11 and 1723 DF, p-value: 1.84e-14
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Saturated model minus the constantsummary(lm(aauw ~ as.factor(totchi) - 1, data = girls))
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## as.factor(totchi)0 56.41 2.76 20.42 <2e-16 ***
## as.factor(totchi)1 61.86 3.05 20.31 <2e-16 ***
## as.factor(totchi)2 52.62 1.75 30.13 <2e-16 ***
## as.factor(totchi)3 42.76 2.07 20.62 <2e-16 ***
## as.factor(totchi)4 37.11 2.90 12.79 <2e-16 ***
## as.factor(totchi)5 40.95 3.99 10.27 <2e-16 ***
## as.factor(totchi)6 22.82 10.05 2.27 0.0233 *
## as.factor(totchi)7 39.29 11.07 3.55 0.0004 ***
## as.factor(totchi)8 1.08 11.96 0.09 0.9278
## as.factor(totchi)9 6.00 23.92 0.25 0.8020
## as.factor(totchi)10 3.00 20.72 0.14 0.8849
## as.factor(totchi)12 0.00 41.43 0.00 1.0000
## ---
## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1
##
## Residual standard error: 41 on 1723 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.587, Adjusted R-squared: 0.584
## F-statistic: 204 on 12 and 1723 DF, p-value: <2e-16
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Compare to within-strata means
The saturated model makes no assumptions about the between-stratarelationships.
Just calculates within-strata means:
c1 <- coef(lm(aauw ~ as.factor(totchi) - 1, data = girls))
c2 <- with(girls, tapply(aauw, totchi, mean, na.rm = TRUE))
rbind(c1, c2)
## 0 1 2 3 4 5 6 7 8 9 10 12
## c1 56 62 53 43 37 41 23 39 1.1 6 3 0
## c2 56 62 53 43 37 41 23 39 1.1 6 3 0
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Compare to within-strata means
The saturated model makes no assumptions about the between-stratarelationships.
Just calculates within-strata means:
c1 <- coef(lm(aauw ~ as.factor(totchi) - 1, data = girls))
c2 <- with(girls, tapply(aauw, totchi, mean, na.rm = TRUE))
rbind(c1, c2)
## 0 1 2 3 4 5 6 7 8 9 10 12
## c1 56 62 53 43 37 41 23 39 1.1 6 3 0
## c2 56 62 53 43 37 41 23 39 1.1 6 3 0
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Compare to within-strata means
The saturated model makes no assumptions about the between-stratarelationships.
Just calculates within-strata means:
c1 <- coef(lm(aauw ~ as.factor(totchi) - 1, data = girls))
c2 <- with(girls, tapply(aauw, totchi, mean, na.rm = TRUE))
rbind(c1, c2)
## 0 1 2 3 4 5 6 7 8 9 10 12
## c1 56 62 53 43 37 41 23 39 1.1 6 3 0
## c2 56 62 53 43 37 41 23 39 1.1 6 3 0
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Other justifications for OLS
Justification 2: X ′i β is the best linear predictor (in a mean-squarederror sense) of Yi .
I Why?
β = arg minb E[(Yi − X ′i b)2]
Justification 3: X ′i β provides the minimum mean squared error linearapproximation to E [Yi |Xi ].
Even if the CEF is not linear, a linear regression provides the bestlinear approximation to that CEF.
Don’t need to believe the assumptions (linearity) in order to useregression as a good approximation to the CEF.
Warning if the CEF is very nonlinear then this approximation could beterrible!!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 78 / 176
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Other justifications for OLS
Justification 2: X ′i β is the best linear predictor (in a mean-squarederror sense) of Yi .
I Why?
β = arg minb E[(Yi − X ′i b)2]
Justification 3: X ′i β provides the minimum mean squared error linearapproximation to E [Yi |Xi ].
Even if the CEF is not linear, a linear regression provides the bestlinear approximation to that CEF.
Don’t need to believe the assumptions (linearity) in order to useregression as a good approximation to the CEF.
Warning if the CEF is very nonlinear then this approximation could beterrible!!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 78 / 176
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Other justifications for OLS
Justification 2: X ′i β is the best linear predictor (in a mean-squarederror sense) of Yi .
I Why? β = arg minb E[(Yi − X ′i b)2]
Justification 3: X ′i β provides the minimum mean squared error linearapproximation to E [Yi |Xi ].
Even if the CEF is not linear, a linear regression provides the bestlinear approximation to that CEF.
Don’t need to believe the assumptions (linearity) in order to useregression as a good approximation to the CEF.
Warning if the CEF is very nonlinear then this approximation could beterrible!!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 78 / 176
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Other justifications for OLS
Justification 2: X ′i β is the best linear predictor (in a mean-squarederror sense) of Yi .
I Why? β = arg minb E[(Yi − X ′i b)2]
Justification 3: X ′i β provides the minimum mean squared error linearapproximation to E [Yi |Xi ].
Even if the CEF is not linear, a linear regression provides the bestlinear approximation to that CEF.
Don’t need to believe the assumptions (linearity) in order to useregression as a good approximation to the CEF.
Warning if the CEF is very nonlinear then this approximation could beterrible!!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 78 / 176
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Other justifications for OLS
Justification 2: X ′i β is the best linear predictor (in a mean-squarederror sense) of Yi .
I Why? β = arg minb E[(Yi − X ′i b)2]
Justification 3: X ′i β provides the minimum mean squared error linearapproximation to E [Yi |Xi ].
Even if the CEF is not linear, a linear regression provides the bestlinear approximation to that CEF.
Don’t need to believe the assumptions (linearity) in order to useregression as a good approximation to the CEF.
Warning if the CEF is very nonlinear then this approximation could beterrible!!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 78 / 176
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Other justifications for OLS
Justification 2: X ′i β is the best linear predictor (in a mean-squarederror sense) of Yi .
I Why? β = arg minb E[(Yi − X ′i b)2]
Justification 3: X ′i β provides the minimum mean squared error linearapproximation to E [Yi |Xi ].
Even if the CEF is not linear, a linear regression provides the bestlinear approximation to that CEF.
Don’t need to believe the assumptions (linearity) in order to useregression as a good approximation to the CEF.
Warning if the CEF is very nonlinear then this approximation could beterrible!!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 78 / 176
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Other justifications for OLS
Justification 2: X ′i β is the best linear predictor (in a mean-squarederror sense) of Yi .
I Why? β = arg minb E[(Yi − X ′i b)2]
Justification 3: X ′i β provides the minimum mean squared error linearapproximation to E [Yi |Xi ].
Even if the CEF is not linear, a linear regression provides the bestlinear approximation to that CEF.
Don’t need to believe the assumptions (linearity) in order to useregression as a good approximation to the CEF.
Warning if the CEF is very nonlinear then this approximation could beterrible!!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 78 / 176
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The error terms
Let’s define the error term: ei ≡ Yi − X ′i β so that:
Yi = X ′i β + [Yi − X ′i β] = X ′i β + ei
Note the residual ei is uncorrelated with Xi :
E[Xiei ] = E[Xi (Yi − X ′i β)]
= E[XiYi ]− E[XiX′i β]
= E[XiYi ]− E[XiX
′i E[XiX
′i ]−1E[XiYi ]
]= E[XiYi ]− E[XiX
′i ]E[XiX
′i ]−1E[XiYi ]
= E[XiYi ]− E[XiYi ] = 0
No assumptions on the linearity of E[Yi |Xi ].
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The error terms
Let’s define the error term: ei ≡ Yi − X ′i β so that:
Yi = X ′i β + [Yi − X ′i β] = X ′i β + ei
Note the residual ei is uncorrelated with Xi :
E[Xiei ] = E[Xi (Yi − X ′i β)]
= E[XiYi ]− E[XiX′i β]
= E[XiYi ]− E[XiX
′i E[XiX
′i ]−1E[XiYi ]
]= E[XiYi ]− E[XiX
′i ]E[XiX
′i ]−1E[XiYi ]
= E[XiYi ]− E[XiYi ] = 0
No assumptions on the linearity of E[Yi |Xi ].
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 79 / 176
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The error terms
Let’s define the error term: ei ≡ Yi − X ′i β so that:
Yi = X ′i β + [Yi − X ′i β] = X ′i β + ei
Note the residual ei is uncorrelated with Xi :
E[Xiei ] = E[Xi (Yi − X ′i β)]
= E[XiYi ]− E[XiX′i β]
= E[XiYi ]− E[XiX
′i E[XiX
′i ]−1E[XiYi ]
]= E[XiYi ]− E[XiX
′i ]E[XiX
′i ]−1E[XiYi ]
= E[XiYi ]− E[XiYi ] = 0
No assumptions on the linearity of E[Yi |Xi ].
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 79 / 176
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The error terms
Let’s define the error term: ei ≡ Yi − X ′i β so that:
Yi = X ′i β + [Yi − X ′i β] = X ′i β + ei
Note the residual ei is uncorrelated with Xi :
E[Xiei ] = E[Xi (Yi − X ′i β)]
= E[XiYi ]− E[XiX′i β]
= E[XiYi ]− E[XiX
′i E[XiX
′i ]−1E[XiYi ]
]= E[XiYi ]− E[XiX
′i ]E[XiX
′i ]−1E[XiYi ]
= E[XiYi ]− E[XiYi ] = 0
No assumptions on the linearity of E[Yi |Xi ].
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 79 / 176
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The error terms
Let’s define the error term: ei ≡ Yi − X ′i β so that:
Yi = X ′i β + [Yi − X ′i β] = X ′i β + ei
Note the residual ei is uncorrelated with Xi :
E[Xiei ] = E[Xi (Yi − X ′i β)]
= E[XiYi ]− E[XiX′i β]
= E[XiYi ]− E[XiX
′i E[XiX
′i ]−1E[XiYi ]
]= E[XiYi ]− E[XiX
′i ]E[XiX
′i ]−1E[XiYi ]
= E[XiYi ]− E[XiYi ] = 0
No assumptions on the linearity of E[Yi |Xi ].
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 79 / 176
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OLS estimator
We know the population value of β is:
β = E[XiX′i ]−1E[XiYi ]
How do we get an estimator of this?
Plug-in principle replace population expectation with sampleversions:
β =
[1
N
∑i
XiX′i
]−11
N
∑i
XiYi
If you work through the matrix algebra, this turns out to be:
β =(X′X
)−1X′y
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OLS estimator
We know the population value of β is:
β = E[XiX′i ]−1E[XiYi ]
How do we get an estimator of this?
Plug-in principle replace population expectation with sampleversions:
β =
[1
N
∑i
XiX′i
]−11
N
∑i
XiYi
If you work through the matrix algebra, this turns out to be:
β =(X′X
)−1X′y
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OLS estimator
We know the population value of β is:
β = E[XiX′i ]−1E[XiYi ]
How do we get an estimator of this?
Plug-in principle replace population expectation with sampleversions:
β =
[1
N
∑i
XiX′i
]−11
N
∑i
XiYi
If you work through the matrix algebra, this turns out to be:
β =(X′X
)−1X′y
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OLS estimator
We know the population value of β is:
β = E[XiX′i ]−1E[XiYi ]
How do we get an estimator of this?
Plug-in principle replace population expectation with sampleversions:
β =
[1
N
∑i
XiX′i
]−11
N
∑i
XiYi
If you work through the matrix algebra, this turns out to be:
β =(X′X
)−1X′y
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 80 / 176
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Asymptotic OLS inference
With this representation in hand, we can write the OLS estimator asfollows:
β = β +
[∑i
XiX′i
]−1∑i
Xiei
Core idea:∑
i Xiei is the sum of r.v.s so the CLT applies.
That, plus some simple asymptotic theory allows us to say:
√N(β − β) N(0,Ω)
Converges in distribution to a Normal distribution with mean vector 0and covariance matrix, Ω:
Ω = E[XiX′i ]−1E[XiX
′i e
2i ]E[XiX
′i ]−1.
No linearity assumption needed!
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Asymptotic OLS inference
With this representation in hand, we can write the OLS estimator asfollows:
β = β +
[∑i
XiX′i
]−1∑i
Xiei
Core idea:∑
i Xiei is the sum of r.v.s so the CLT applies.
That, plus some simple asymptotic theory allows us to say:
√N(β − β) N(0,Ω)
Converges in distribution to a Normal distribution with mean vector 0and covariance matrix, Ω:
Ω = E[XiX′i ]−1E[XiX
′i e
2i ]E[XiX
′i ]−1.
No linearity assumption needed!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 81 / 176
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Asymptotic OLS inference
With this representation in hand, we can write the OLS estimator asfollows:
β = β +
[∑i
XiX′i
]−1∑i
Xiei
Core idea:∑
i Xiei is the sum of r.v.s so the CLT applies.
That, plus some simple asymptotic theory allows us to say:
√N(β − β) N(0,Ω)
Converges in distribution to a Normal distribution with mean vector 0and covariance matrix, Ω:
Ω = E[XiX′i ]−1E[XiX
′i e
2i ]E[XiX
′i ]−1.
No linearity assumption needed!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 81 / 176
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Asymptotic OLS inference
With this representation in hand, we can write the OLS estimator asfollows:
β = β +
[∑i
XiX′i
]−1∑i
Xiei
Core idea:∑
i Xiei is the sum of r.v.s so the CLT applies.
That, plus some simple asymptotic theory allows us to say:
√N(β − β) N(0,Ω)
Converges in distribution to a Normal distribution with mean vector 0and covariance matrix, Ω:
Ω = E[XiX′i ]−1E[XiX
′i e
2i ]E[XiX
′i ]−1.
No linearity assumption needed!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 81 / 176
![Page 333: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/333.jpg)
Asymptotic OLS inference
With this representation in hand, we can write the OLS estimator asfollows:
β = β +
[∑i
XiX′i
]−1∑i
Xiei
Core idea:∑
i Xiei is the sum of r.v.s so the CLT applies.
That, plus some simple asymptotic theory allows us to say:
√N(β − β) N(0,Ω)
Converges in distribution to a Normal distribution with mean vector 0and covariance matrix, Ω:
Ω = E[XiX′i ]−1E[XiX
′i e
2i ]E[XiX
′i ]−1.
No linearity assumption needed!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 81 / 176
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Estimating the variance
In large samples then:
√N(β − β) ∼ N(0,Ω)
How to estimate Ω? Plug-in principle again!
Ω =
[∑i
XiX′i
]−1 [∑i
XiX′i e
2i
][∑i
XiX′i
]−1
.
Replace ei with its emprical counterpart (residuals) ei = Yi − X ′i β.
Replace the population moments of Xi with their sample counterparts.
The square root of the diagonals of this covariance matrix are the“robust” or Huber-White standard errors.
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Estimating the variance
In large samples then:
√N(β − β) ∼ N(0,Ω)
How to estimate Ω? Plug-in principle again!
Ω =
[∑i
XiX′i
]−1 [∑i
XiX′i e
2i
][∑i
XiX′i
]−1
.
Replace ei with its emprical counterpart (residuals) ei = Yi − X ′i β.
Replace the population moments of Xi with their sample counterparts.
The square root of the diagonals of this covariance matrix are the“robust” or Huber-White standard errors.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 82 / 176
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Estimating the variance
In large samples then:
√N(β − β) ∼ N(0,Ω)
How to estimate Ω? Plug-in principle again!
Ω =
[∑i
XiX′i
]−1 [∑i
XiX′i e
2i
][∑i
XiX′i
]−1
.
Replace ei with its emprical counterpart (residuals) ei = Yi − X ′i β.
Replace the population moments of Xi with their sample counterparts.
The square root of the diagonals of this covariance matrix are the“robust” or Huber-White standard errors.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 82 / 176
![Page 337: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/337.jpg)
Estimating the variance
In large samples then:
√N(β − β) ∼ N(0,Ω)
How to estimate Ω? Plug-in principle again!
Ω =
[∑i
XiX′i
]−1 [∑i
XiX′i e
2i
][∑i
XiX′i
]−1
.
Replace ei with its emprical counterpart (residuals) ei = Yi − X ′i β.
Replace the population moments of Xi with their sample counterparts.
The square root of the diagonals of this covariance matrix are the“robust” or Huber-White standard errors.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 82 / 176
![Page 338: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/338.jpg)
Estimating the variance
In large samples then:
√N(β − β) ∼ N(0,Ω)
How to estimate Ω? Plug-in principle again!
Ω =
[∑i
XiX′i
]−1 [∑i
XiX′i e
2i
][∑i
XiX′i
]−1
.
Replace ei with its emprical counterpart (residuals) ei = Yi − X ′i β.
Replace the population moments of Xi with their sample counterparts.
The square root of the diagonals of this covariance matrix are the“robust” or Huber-White standard errors.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 82 / 176
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Agnostic Statistics
The key insight here is that we can derive estimators under somewhatweaker assumptions
They still rely heavily on large samples (asymptotic results) andindependent samples.
See Aronow and Miller for much more.
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Agnostic Statistics
The key insight here is that we can derive estimators under somewhatweaker assumptions
They still rely heavily on large samples (asymptotic results) andindependent samples.
See Aronow and Miller for much more.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 83 / 176
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Agnostic Statistics
The key insight here is that we can derive estimators under somewhatweaker assumptions
They still rely heavily on large samples (asymptotic results) andindependent samples.
See Aronow and Miller for much more.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 83 / 176
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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Regression and causality
Most econometrics textbooks: regression defined without respect tocausality.
But then when is β “biased”? What does this even mean?
The question, then, is when does knowing the CEF tell us somethingabout causality?
Angrist and Pishke argues that a regression is causal when the CEF itapproximates is causal. Identification is king.
We will show that under certain conditions, a regression of theoutcome on the treatment and the covariates can recover a causalparameter, but perhaps not the one in which we are interested.
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Regression and causality
Most econometrics textbooks: regression defined without respect tocausality.
But then when is β “biased”? What does this even mean?
The question, then, is when does knowing the CEF tell us somethingabout causality?
Angrist and Pishke argues that a regression is causal when the CEF itapproximates is causal. Identification is king.
We will show that under certain conditions, a regression of theoutcome on the treatment and the covariates can recover a causalparameter, but perhaps not the one in which we are interested.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 85 / 176
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Regression and causality
Most econometrics textbooks: regression defined without respect tocausality.
But then when is β “biased”? What does this even mean?
The question, then, is when does knowing the CEF tell us somethingabout causality?
Angrist and Pishke argues that a regression is causal when the CEF itapproximates is causal. Identification is king.
We will show that under certain conditions, a regression of theoutcome on the treatment and the covariates can recover a causalparameter, but perhaps not the one in which we are interested.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 85 / 176
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Regression and causality
Most econometrics textbooks: regression defined without respect tocausality.
But then when is β “biased”? What does this even mean?
The question, then, is when does knowing the CEF tell us somethingabout causality?
Angrist and Pishke argues that a regression is causal when the CEF itapproximates is causal. Identification is king.
We will show that under certain conditions, a regression of theoutcome on the treatment and the covariates can recover a causalparameter, but perhaps not the one in which we are interested.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 85 / 176
![Page 348: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/348.jpg)
Regression and causality
Most econometrics textbooks: regression defined without respect tocausality.
But then when is β “biased”? What does this even mean?
The question, then, is when does knowing the CEF tell us somethingabout causality?
Angrist and Pishke argues that a regression is causal when the CEF itapproximates is causal. Identification is king.
We will show that under certain conditions, a regression of theoutcome on the treatment and the covariates can recover a causalparameter, but perhaps not the one in which we are interested.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 85 / 176
![Page 349: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/349.jpg)
Linear constant effects model, binary treatment
Now with the benefit of covering agnostic regression, let’s review again thesimple case.
Experiment: with a simple experiment, we can rewrite the consistencyassumption to be a regression formula:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= E[Yi (0)] + τDi + (Yi (0)− E[Yi (0)])
= µ0 + τDi + v0i
Note that if ignorability holds (as in an experiment) for Yi (0), then itwill also hold for v0
i , since E[Yi (0)] is constant. Thus, this satifies theusual assumptions for regression.
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Linear constant effects model, binary treatment
Now with the benefit of covering agnostic regression, let’s review again thesimple case.
Experiment: with a simple experiment, we can rewrite the consistencyassumption to be a regression formula:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= E[Yi (0)] + τDi + (Yi (0)− E[Yi (0)])
= µ0 + τDi + v0i
Note that if ignorability holds (as in an experiment) for Yi (0), then itwill also hold for v0
i , since E[Yi (0)] is constant. Thus, this satifies theusual assumptions for regression.
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Linear constant effects model, binary treatment
Now with the benefit of covering agnostic regression, let’s review again thesimple case.
Experiment: with a simple experiment, we can rewrite the consistencyassumption to be a regression formula:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= E[Yi (0)] + τDi + (Yi (0)− E[Yi (0)])
= µ0 + τDi + v0i
Note that if ignorability holds (as in an experiment) for Yi (0), then itwill also hold for v0
i , since E[Yi (0)] is constant. Thus, this satifies theusual assumptions for regression.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 86 / 176
![Page 352: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/352.jpg)
Linear constant effects model, binary treatment
Now with the benefit of covering agnostic regression, let’s review again thesimple case.
Experiment: with a simple experiment, we can rewrite the consistencyassumption to be a regression formula:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= E[Yi (0)] + τDi + (Yi (0)− E[Yi (0)])
= µ0 + τDi + v0i
Note that if ignorability holds (as in an experiment) for Yi (0), then itwill also hold for v0
i , since E[Yi (0)] is constant. Thus, this satifies theusual assumptions for regression.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 86 / 176
![Page 353: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/353.jpg)
Linear constant effects model, binary treatment
Now with the benefit of covering agnostic regression, let’s review again thesimple case.
Experiment: with a simple experiment, we can rewrite the consistencyassumption to be a regression formula:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= E[Yi (0)] + τDi + (Yi (0)− E[Yi (0)])
= µ0 + τDi + v0i
Note that if ignorability holds (as in an experiment) for Yi (0), then itwill also hold for v0
i , since E[Yi (0)] is constant. Thus, this satifies theusual assumptions for regression.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 86 / 176
![Page 354: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/354.jpg)
Linear constant effects model, binary treatment
Now with the benefit of covering agnostic regression, let’s review again thesimple case.
Experiment: with a simple experiment, we can rewrite the consistencyassumption to be a regression formula:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= E[Yi (0)] + τDi + (Yi (0)− E[Yi (0)])
= µ0 + τDi + v0i
Note that if ignorability holds (as in an experiment) for Yi (0), then itwill also hold for v0
i , since E[Yi (0)] is constant. Thus, this satifies theusual assumptions for regression.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 86 / 176
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Now with covariates
Now assume no unmeasured confounders: Yi (d)⊥⊥Di |Xi .
We will assume a linear model for the potential outcomes:
Yi (d) = α + τ · d + ηi
Remember that linearity isn’t an assumption if Di is binary
Effect of Di is constant here, the ηi are the only source of individualvariation and we have E [ηi ] = 0.
Consistency assumption allows us to write this as:
Yi = α + τDi + ηi .
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Now with covariates
Now assume no unmeasured confounders: Yi (d)⊥⊥Di |Xi .
We will assume a linear model for the potential outcomes:
Yi (d) = α + τ · d + ηi
Remember that linearity isn’t an assumption if Di is binary
Effect of Di is constant here, the ηi are the only source of individualvariation and we have E [ηi ] = 0.
Consistency assumption allows us to write this as:
Yi = α + τDi + ηi .
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Now with covariates
Now assume no unmeasured confounders: Yi (d)⊥⊥Di |Xi .
We will assume a linear model for the potential outcomes:
Yi (d) = α + τ · d + ηi
Remember that linearity isn’t an assumption if Di is binary
Effect of Di is constant here, the ηi are the only source of individualvariation and we have E [ηi ] = 0.
Consistency assumption allows us to write this as:
Yi = α + τDi + ηi .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 87 / 176
![Page 358: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/358.jpg)
Now with covariates
Now assume no unmeasured confounders: Yi (d)⊥⊥Di |Xi .
We will assume a linear model for the potential outcomes:
Yi (d) = α + τ · d + ηi
Remember that linearity isn’t an assumption if Di is binary
Effect of Di is constant here, the ηi are the only source of individualvariation and we have E [ηi ] = 0.
Consistency assumption allows us to write this as:
Yi = α + τDi + ηi .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 87 / 176
![Page 359: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/359.jpg)
Now with covariates
Now assume no unmeasured confounders: Yi (d)⊥⊥Di |Xi .
We will assume a linear model for the potential outcomes:
Yi (d) = α + τ · d + ηi
Remember that linearity isn’t an assumption if Di is binary
Effect of Di is constant here, the ηi are the only source of individualvariation and we have E [ηi ] = 0.
Consistency assumption allows us to write this as:
Yi = α + τDi + ηi .
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 87 / 176
![Page 360: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/360.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νi
New error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ] = α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 361: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/361.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νiNew error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ] = α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 362: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/362.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νiNew error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ] = α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 363: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/363.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νiNew error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ] = α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 364: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/364.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νiNew error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ]
= α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 365: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/365.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νiNew error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ] = α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 366: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/366.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νiNew error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ] = α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 367: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/367.jpg)
Covariates in the error
Let’s assume that ηi is linear in Xi : ηi = X ′i γ + νiNew error is uncorrelated with Xi : E[νi |Xi ] = 0.
This is an assumption! Might be false!
Plug into the above:
E[Yi (d)|Xi ] = E [Yi |Di ,Xi ] = α + τDi + E [ηi |Xi ]
= α + τDi + X ′i γ + E [νi |Xi ]
= α + τDi + X ′i γ
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 88 / 176
![Page 368: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/368.jpg)
Summing up regression with constant effects
Reviewing the assumptions we’ve used:
I no unmeasured confoundersI constant treatment effectsI linearity of the treatment/covariates
Under these, we can run the following regression to estimate the ATE,τ :
Yi = α + τDi + X ′i γ + νi
Works with continuous or ordinal Di if effect of these variables is trulylinear.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 89 / 176
![Page 369: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/369.jpg)
Summing up regression with constant effects
Reviewing the assumptions we’ve used:
I no unmeasured confounders
I constant treatment effectsI linearity of the treatment/covariates
Under these, we can run the following regression to estimate the ATE,τ :
Yi = α + τDi + X ′i γ + νi
Works with continuous or ordinal Di if effect of these variables is trulylinear.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 89 / 176
![Page 370: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/370.jpg)
Summing up regression with constant effects
Reviewing the assumptions we’ve used:
I no unmeasured confoundersI constant treatment effects
I linearity of the treatment/covariates
Under these, we can run the following regression to estimate the ATE,τ :
Yi = α + τDi + X ′i γ + νi
Works with continuous or ordinal Di if effect of these variables is trulylinear.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 89 / 176
![Page 371: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/371.jpg)
Summing up regression with constant effects
Reviewing the assumptions we’ve used:
I no unmeasured confoundersI constant treatment effectsI linearity of the treatment/covariates
Under these, we can run the following regression to estimate the ATE,τ :
Yi = α + τDi + X ′i γ + νi
Works with continuous or ordinal Di if effect of these variables is trulylinear.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 89 / 176
![Page 372: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/372.jpg)
Summing up regression with constant effects
Reviewing the assumptions we’ve used:
I no unmeasured confoundersI constant treatment effectsI linearity of the treatment/covariates
Under these, we can run the following regression to estimate the ATE,τ :
Yi = α + τDi + X ′i γ + νi
Works with continuous or ordinal Di if effect of these variables is trulylinear.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 89 / 176
![Page 373: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/373.jpg)
Summing up regression with constant effects
Reviewing the assumptions we’ve used:
I no unmeasured confoundersI constant treatment effectsI linearity of the treatment/covariates
Under these, we can run the following regression to estimate the ATE,τ :
Yi = α + τDi + X ′i γ + νi
Works with continuous or ordinal Di if effect of these variables is trulylinear.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 89 / 176
![Page 374: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/374.jpg)
1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 90 / 176
![Page 375: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/375.jpg)
1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 90 / 176
![Page 376: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/376.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 377: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/377.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 378: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/378.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 379: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/379.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 380: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/380.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 381: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/381.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 382: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/382.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 383: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/383.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)
2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 384: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/384.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 385: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/385.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 386: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/386.jpg)
Heterogeneous effects, binary treatment
Completely randomized experiment:
Yi = DiYi (1) + (1− Di )Yi (0)
= Yi (0) + (Yi (1)− Yi (0))Di
= µ0 + τiDi + (Yi (0)− µ0)
= µ0 + τDi + (Yi (0)− µ0) + (τi − τ) · Di
= µ0 + τDi + εi
Error term now includes two components:
1 “Baseline” variation in the outcome: (Yi (0)− µ0)2 Variation in the treatment effect, (τi − τ)
We can verify that under experiment, E[εi |Di ] = 0
Thus, OLS estimates the ATE with no covariates.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 91 / 176
![Page 387: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/387.jpg)
Adding covariates
What happens with no unmeasured confounders? Need to conditionon Xi now.
Remember identification of the ATE/ATT using iterated expectations.
ATE is the weighted sum of Conditional Average Treatment Effects(CATEs):
τ =∑x
τ(x) Pr[Xi = x ]
ATE/ATT are weighted averages of CATEs.
What about the regression estimand, τR? How does it relate to theATE/ATT?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 92 / 176
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Adding covariates
What happens with no unmeasured confounders? Need to conditionon Xi now.
Remember identification of the ATE/ATT using iterated expectations.
ATE is the weighted sum of Conditional Average Treatment Effects(CATEs):
τ =∑x
τ(x) Pr[Xi = x ]
ATE/ATT are weighted averages of CATEs.
What about the regression estimand, τR? How does it relate to theATE/ATT?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 92 / 176
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Adding covariates
What happens with no unmeasured confounders? Need to conditionon Xi now.
Remember identification of the ATE/ATT using iterated expectations.
ATE is the weighted sum of Conditional Average Treatment Effects(CATEs):
τ =∑x
τ(x) Pr[Xi = x ]
ATE/ATT are weighted averages of CATEs.
What about the regression estimand, τR? How does it relate to theATE/ATT?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 92 / 176
![Page 390: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/390.jpg)
Adding covariates
What happens with no unmeasured confounders? Need to conditionon Xi now.
Remember identification of the ATE/ATT using iterated expectations.
ATE is the weighted sum of Conditional Average Treatment Effects(CATEs):
τ =∑x
τ(x) Pr[Xi = x ]
ATE/ATT are weighted averages of CATEs.
What about the regression estimand, τR? How does it relate to theATE/ATT?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 92 / 176
![Page 391: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/391.jpg)
Adding covariates
What happens with no unmeasured confounders? Need to conditionon Xi now.
Remember identification of the ATE/ATT using iterated expectations.
ATE is the weighted sum of Conditional Average Treatment Effects(CATEs):
τ =∑x
τ(x) Pr[Xi = x ]
ATE/ATT are weighted averages of CATEs.
What about the regression estimand, τR? How does it relate to theATE/ATT?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 92 / 176
![Page 392: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/392.jpg)
Heterogeneous effects and regression
Let’s investigate this under a saturated regression model:
Yi =∑x
Bxiαx + τRDi + ei .
Use a dummy variable for each unique combination of Xi :Bxi = I(Xi = x)
Linear in Xi by construction!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 93 / 176
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Heterogeneous effects and regression
Let’s investigate this under a saturated regression model:
Yi =∑x
Bxiαx + τRDi + ei .
Use a dummy variable for each unique combination of Xi :Bxi = I(Xi = x)
Linear in Xi by construction!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 93 / 176
![Page 394: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/394.jpg)
Heterogeneous effects and regression
Let’s investigate this under a saturated regression model:
Yi =∑x
Bxiαx + τRDi + ei .
Use a dummy variable for each unique combination of Xi :Bxi = I(Xi = x)
Linear in Xi by construction!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 93 / 176
![Page 395: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/395.jpg)
Investigating the regression coefficient
How can we investigate τR? Well, we can rely on the regressionanatomy:
τR =Cov(Yi ,Di − E [Di |Xi ])
Var(Di − E [Di |Xi ])
Di − E[Di |Xi ] is the residual from a regression of Di on the full set ofdummies.
With a little work we can show:
τR =E[τ(Xi )(Di − E[Di |Xi ])
2]
E[(Di − E [Di |Xi ])2]=
E[τ(Xi )σ2d(Xi )]
E[σ2d(Xi )]
σ2d(x) = Var[Di |Xi = x ] is the conditional variance of treatment
assignment.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 94 / 176
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Investigating the regression coefficient
How can we investigate τR? Well, we can rely on the regressionanatomy:
τR =Cov(Yi ,Di − E [Di |Xi ])
Var(Di − E [Di |Xi ])
Di − E[Di |Xi ] is the residual from a regression of Di on the full set ofdummies.
With a little work we can show:
τR =E[τ(Xi )(Di − E[Di |Xi ])
2]
E[(Di − E [Di |Xi ])2]=
E[τ(Xi )σ2d(Xi )]
E[σ2d(Xi )]
σ2d(x) = Var[Di |Xi = x ] is the conditional variance of treatment
assignment.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 94 / 176
![Page 397: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/397.jpg)
Investigating the regression coefficient
How can we investigate τR? Well, we can rely on the regressionanatomy:
τR =Cov(Yi ,Di − E [Di |Xi ])
Var(Di − E [Di |Xi ])
Di − E[Di |Xi ] is the residual from a regression of Di on the full set ofdummies.
With a little work we can show:
τR =E[τ(Xi )(Di − E[Di |Xi ])
2]
E[(Di − E [Di |Xi ])2]=
E[τ(Xi )σ2d(Xi )]
E[σ2d(Xi )]
σ2d(x) = Var[Di |Xi = x ] is the conditional variance of treatment
assignment.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 94 / 176
![Page 398: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/398.jpg)
Investigating the regression coefficient
How can we investigate τR? Well, we can rely on the regressionanatomy:
τR =Cov(Yi ,Di − E [Di |Xi ])
Var(Di − E [Di |Xi ])
Di − E[Di |Xi ] is the residual from a regression of Di on the full set ofdummies.
With a little work we can show:
τR =E[τ(Xi )(Di − E[Di |Xi ])
2]
E[(Di − E [Di |Xi ])2]=
E[τ(Xi )σ2d(Xi )]
E[σ2d(Xi )]
σ2d(x) = Var[Di |Xi = x ] is the conditional variance of treatment
assignment.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 94 / 176
![Page 399: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/399.jpg)
ATE versus OLS
τR = E[τ(Xi )Wi ] =∑x
τ(x)σ2d(x)
E[σ2d(Xi )]
P[Xi = x ]
Compare to the ATE:
τ = E[τ(Xi )] =∑x
τ(x)P[Xi = x ]
Both weight strata relative to their size (P[Xi = x ])
OLS weights strata higher if the treatment variance in those strata(σ2
d(x)) is higher in those strata relative to the average varianceacross strata (E[σ2
d(Xi )]).
The ATE weights only by their size.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 95 / 176
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ATE versus OLS
τR = E[τ(Xi )Wi ] =∑x
τ(x)σ2d(x)
E[σ2d(Xi )]
P[Xi = x ]
Compare to the ATE:
τ = E[τ(Xi )] =∑x
τ(x)P[Xi = x ]
Both weight strata relative to their size (P[Xi = x ])
OLS weights strata higher if the treatment variance in those strata(σ2
d(x)) is higher in those strata relative to the average varianceacross strata (E[σ2
d(Xi )]).
The ATE weights only by their size.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 95 / 176
![Page 401: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/401.jpg)
ATE versus OLS
τR = E[τ(Xi )Wi ] =∑x
τ(x)σ2d(x)
E[σ2d(Xi )]
P[Xi = x ]
Compare to the ATE:
τ = E[τ(Xi )] =∑x
τ(x)P[Xi = x ]
Both weight strata relative to their size (P[Xi = x ])
OLS weights strata higher if the treatment variance in those strata(σ2
d(x)) is higher in those strata relative to the average varianceacross strata (E[σ2
d(Xi )]).
The ATE weights only by their size.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 95 / 176
![Page 402: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/402.jpg)
ATE versus OLS
τR = E[τ(Xi )Wi ] =∑x
τ(x)σ2d(x)
E[σ2d(Xi )]
P[Xi = x ]
Compare to the ATE:
τ = E[τ(Xi )] =∑x
τ(x)P[Xi = x ]
Both weight strata relative to their size (P[Xi = x ])
OLS weights strata higher if the treatment variance in those strata(σ2
d(x)) is higher in those strata relative to the average varianceacross strata (E[σ2
d(Xi )]).
The ATE weights only by their size.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 95 / 176
![Page 403: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/403.jpg)
ATE versus OLS
τR = E[τ(Xi )Wi ] =∑x
τ(x)σ2d(x)
E[σ2d(Xi )]
P[Xi = x ]
Compare to the ATE:
τ = E[τ(Xi )] =∑x
τ(x)P[Xi = x ]
Both weight strata relative to their size (P[Xi = x ])
OLS weights strata higher if the treatment variance in those strata(σ2
d(x)) is higher in those strata relative to the average varianceacross strata (E[σ2
d(Xi )]).
The ATE weights only by their size.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 95 / 176
![Page 404: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/404.jpg)
Regression weighting
Wi =σ2d(Xi )
E[σ2d(Xi )]
Why does OLS weight like this?
OLS is a minimum-variance estimator more weight to more precisewithin-strata estimates.
Within-strata estimates are most precise when the treatment is evenlyspread and thus has the highest variance.
If Di is binary, then we know the conditional variance will be:
σ2d(x) = P[Di = 1|Xi = x ] (1− P[Di = 1|Xi = x ])
Maximum variance with P[Di = 1|Xi = x ] = 1/2.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 96 / 176
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Regression weighting
Wi =σ2d(Xi )
E[σ2d(Xi )]
Why does OLS weight like this?
OLS is a minimum-variance estimator more weight to more precisewithin-strata estimates.
Within-strata estimates are most precise when the treatment is evenlyspread and thus has the highest variance.
If Di is binary, then we know the conditional variance will be:
σ2d(x) = P[Di = 1|Xi = x ] (1− P[Di = 1|Xi = x ])
Maximum variance with P[Di = 1|Xi = x ] = 1/2.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 96 / 176
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Regression weighting
Wi =σ2d(Xi )
E[σ2d(Xi )]
Why does OLS weight like this?
OLS is a minimum-variance estimator more weight to more precisewithin-strata estimates.
Within-strata estimates are most precise when the treatment is evenlyspread and thus has the highest variance.
If Di is binary, then we know the conditional variance will be:
σ2d(x) = P[Di = 1|Xi = x ] (1− P[Di = 1|Xi = x ])
Maximum variance with P[Di = 1|Xi = x ] = 1/2.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 96 / 176
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Regression weighting
Wi =σ2d(Xi )
E[σ2d(Xi )]
Why does OLS weight like this?
OLS is a minimum-variance estimator more weight to more precisewithin-strata estimates.
Within-strata estimates are most precise when the treatment is evenlyspread and thus has the highest variance.
If Di is binary, then we know the conditional variance will be:
σ2d(x) = P[Di = 1|Xi = x ] (1− P[Di = 1|Xi = x ])
Maximum variance with P[Di = 1|Xi = x ] = 1/2.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 96 / 176
![Page 408: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/408.jpg)
Regression weighting
Wi =σ2d(Xi )
E[σ2d(Xi )]
Why does OLS weight like this?
OLS is a minimum-variance estimator more weight to more precisewithin-strata estimates.
Within-strata estimates are most precise when the treatment is evenlyspread and thus has the highest variance.
If Di is binary, then we know the conditional variance will be:
σ2d(x) = P[Di = 1|Xi = x ] (1− P[Di = 1|Xi = x ])
Maximum variance with P[Di = 1|Xi = x ] = 1/2.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 96 / 176
![Page 409: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/409.jpg)
Regression weighting
Wi =σ2d(Xi )
E[σ2d(Xi )]
Why does OLS weight like this?
OLS is a minimum-variance estimator more weight to more precisewithin-strata estimates.
Within-strata estimates are most precise when the treatment is evenlyspread and thus has the highest variance.
If Di is binary, then we know the conditional variance will be:
σ2d(x) = P[Di = 1|Xi = x ] (1− P[Di = 1|Xi = x ])
Maximum variance with P[Di = 1|Xi = x ] = 1/2.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 96 / 176
![Page 410: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/410.jpg)
OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
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OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
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OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
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OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
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OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
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OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5
Average conditional variance: E[σ2d(Xi )] = 0.13
weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
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OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13
weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
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OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
![Page 418: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/418.jpg)
OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
![Page 419: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/419.jpg)
OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
![Page 420: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/420.jpg)
OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
![Page 421: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/421.jpg)
OLS weighting exampleBinary covariate:
Group 1 Group 2
P[Xi = 1] = 0.75 P[Xi = 0] = 0.25
P[Di = 1|Xi = 1] = 0.9 P[Di = 1|Xi = 0] = 0.5
σ2d(1) = 0.09 σ2
d(0) = 0.25
τ(1) = 1 τ(0) = −1
Implies the ATE is τ = 0.5Average conditional variance: E[σ2
d(Xi )] = 0.13 weights for Xi = 1 are: 0.09/0.13 = 0.692, for Xi = 0: 0.25/0.13= 1.92.
τR = E[τ(Xi )Wi ]
= τ(1)W (1)P[Xi = 1] + τ(0)W (0)P[Xi = 0]
= 1× 0.692× 0.75 +−1× 1.92× 0.25
= 0.039
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 97 / 176
![Page 422: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/422.jpg)
When will OLS estimate the ATE?
When does τ = τR?
Constant treatment effects: τ(x) = τ = τRConstant probability of treatment: e(x) = P[Di = 1|Xi = x ] = e.
I Implies that the OLS weights are 1.
Incorrect linearity assumption in Xi will lead to more bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 98 / 176
![Page 423: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/423.jpg)
When will OLS estimate the ATE?
When does τ = τR?
Constant treatment effects: τ(x) = τ = τR
Constant probability of treatment: e(x) = P[Di = 1|Xi = x ] = e.
I Implies that the OLS weights are 1.
Incorrect linearity assumption in Xi will lead to more bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 98 / 176
![Page 424: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/424.jpg)
When will OLS estimate the ATE?
When does τ = τR?
Constant treatment effects: τ(x) = τ = τRConstant probability of treatment: e(x) = P[Di = 1|Xi = x ] = e.
I Implies that the OLS weights are 1.
Incorrect linearity assumption in Xi will lead to more bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 98 / 176
![Page 425: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/425.jpg)
When will OLS estimate the ATE?
When does τ = τR?
Constant treatment effects: τ(x) = τ = τRConstant probability of treatment: e(x) = P[Di = 1|Xi = x ] = e.
I Implies that the OLS weights are 1.
Incorrect linearity assumption in Xi will lead to more bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 98 / 176
![Page 426: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/426.jpg)
When will OLS estimate the ATE?
When does τ = τR?
Constant treatment effects: τ(x) = τ = τRConstant probability of treatment: e(x) = P[Di = 1|Xi = x ] = e.
I Implies that the OLS weights are 1.
Incorrect linearity assumption in Xi will lead to more bias.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 98 / 176
![Page 427: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/427.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 428: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/428.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)
I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 429: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/429.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 430: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/430.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 431: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/431.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 432: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/432.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 433: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/433.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 434: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/434.jpg)
Other ways to use regression
What’s the path forward?
I Accept the bias (might be relatively small with saturated models)I Use a different regression approach
Let µd(x) = E[Yi (d)|Xi = x ] be the CEF for the potential outcomeunder Di = d .
By consistency and n.u.c., we have µd(x) = E[Yi |Di = d ,Xi = x ].
Estimate a regression of Yi on Xi among the Di = d group.
Then, µd(x) is just a predicted value from the regression for Xi = x .
How can we use this?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 99 / 176
![Page 435: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/435.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 436: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/436.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 437: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/437.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 438: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/438.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 439: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/439.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 440: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/440.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 441: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/441.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 442: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/442.jpg)
Imputation estimators
Impute the treated potential outcomes with Yi (1) = µ1(Xi )!
Impute the control potential outcomes with Yi (0) = µ0(Xi )!
Procedure:
I Regress Yi on Xi in the treated group and get predicted values for allunits (treated or control).
I Regress Yi on Xi in the control group and get predicted values for allunits (treated or control).
I Take the average difference between these predicted values.
More mathematically, look like this:
τimp =1
N
∑i
µ1(Xi )− µ0(Xi )
Sometimes called an imputation estimator.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 100 / 176
![Page 443: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/443.jpg)
Simple imputation estimator
Use predict() from the within-group models on the data from theentire sample.
Useful trick: use a model on the entire data and model.frame() toget the right design matrix:
## heterogeneous effects
y.het <- ifelse(d == 1, y + rnorm(n, 0, 5), y)
mod <- lm(y.het ~ d + X)
mod1 <- lm(y.het ~ X, subset = d == 1)
mod0 <- lm(y.het ~ X, subset = d == 0)
y1.imps <- predict(mod1, model.frame(mod))
y0.imps <- predict(mod0, model.frame(mod))
mean(y1.imps - y0.imps)
## [1] 0.61
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 101 / 176
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Simple imputation estimator
Use predict() from the within-group models on the data from theentire sample.
Useful trick: use a model on the entire data and model.frame() toget the right design matrix:
## heterogeneous effects
y.het <- ifelse(d == 1, y + rnorm(n, 0, 5), y)
mod <- lm(y.het ~ d + X)
mod1 <- lm(y.het ~ X, subset = d == 1)
mod0 <- lm(y.het ~ X, subset = d == 0)
y1.imps <- predict(mod1, model.frame(mod))
y0.imps <- predict(mod0, model.frame(mod))
mean(y1.imps - y0.imps)
## [1] 0.61
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 101 / 176
![Page 445: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/445.jpg)
Simple imputation estimator
Use predict() from the within-group models on the data from theentire sample.
Useful trick: use a model on the entire data and model.frame() toget the right design matrix:
## heterogeneous effects
y.het <- ifelse(d == 1, y + rnorm(n, 0, 5), y)
mod <- lm(y.het ~ d + X)
mod1 <- lm(y.het ~ X, subset = d == 1)
mod0 <- lm(y.het ~ X, subset = d == 0)
y1.imps <- predict(mod1, model.frame(mod))
y0.imps <- predict(mod0, model.frame(mod))
mean(y1.imps - y0.imps)
## [1] 0.61
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Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βdRecent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etcI Easiest is generalized additive models (GAMs)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 102 / 176
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Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βdRecent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etcI Easiest is generalized additive models (GAMs)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 102 / 176
![Page 448: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/448.jpg)
Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.
I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βdRecent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etcI Easiest is generalized additive models (GAMs)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 102 / 176
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Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βdRecent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etcI Easiest is generalized additive models (GAMs)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 102 / 176
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Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βd
Recent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etcI Easiest is generalized additive models (GAMs)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 102 / 176
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Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βdRecent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etcI Easiest is generalized additive models (GAMs)
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Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βdRecent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etc
I Easiest is generalized additive models (GAMs)
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Notes on imputation estimators
If µd(x) are consistent estimators, then τimp is consistent for the ATE.
Why don’t people use this?
I Most people don’t know the results we’ve been talking about.I Harder to implement than vanilla OLS.
Can use linear regression to estimate µd(x) = x ′βdRecent trend is to estimate µd(x) via non-parametric methods suchas:
I Kernel regression, local linear regression, regression trees, etcI Easiest is generalized additive models (GAMs)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 102 / 176
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Imputation estimator visualization
-2 -1 0 1 2 3
-0.5
0.0
0.5
1.0
1.5
x
y
TreatedControl
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Imputation estimator visualization
-2 -1 0 1 2 3
-0.5
0.0
0.5
1.0
1.5
x
y
TreatedControl
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Imputation estimator visualization
-2 -1 0 1 2 3
-0.5
0.0
0.5
1.0
1.5
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y
TreatedControl
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Nonlinear relationships
Same idea but with nonlinear relationship between Yi and Xi :
-2 -1 0 1 2
-1.0
-0.5
0.0
0.5
x
y
TreatedControl
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Nonlinear relationships
Same idea but with nonlinear relationship between Yi and Xi :
-2 -1 0 1 2
-1.0
-0.5
0.0
0.5
x
y
TreatedControl
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Nonlinear relationships
Same idea but with nonlinear relationship between Yi and Xi :
-2 -1 0 1 2
-1.0
-0.5
0.0
0.5
x
y
TreatedControl
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Using semiparametric regressionHere, CEFs are nonlinear, but we don’t know their form.
We can use GAMs from the mgcv package to for flexible estimate:
library(mgcv)
mod0 <- gam(y ~ s(x), subset = d == 0)
summary(mod0)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## y ~ s(x)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0225 0.0154 -1.46 0.16
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(x) 6.03 7.08 41.3 <2e-16 ***
## ---
## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1
##
## R-sq.(adj) = 0.909 Deviance explained = 92.8%
## GCV = 0.0093204 Scale est. = 0.0071351 n = 30
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Using semiparametric regressionHere, CEFs are nonlinear, but we don’t know their form.
We can use GAMs from the mgcv package to for flexible estimate:
library(mgcv)
mod0 <- gam(y ~ s(x), subset = d == 0)
summary(mod0)
##
## Family: gaussian
## Link function: identity
##
## Formula:
## y ~ s(x)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0225 0.0154 -1.46 0.16
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(x) 6.03 7.08 41.3 <2e-16 ***
## ---
## Signif. codes: 0 ’***’ 0.001 ’**’ 0.01 ’*’ 0.05 ’.’ 0.1 ’ ’ 1
##
## R-sq.(adj) = 0.909 Deviance explained = 92.8%
## GCV = 0.0093204 Scale est. = 0.0071351 n = 30
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Using GAMs
-2 -1 0 1 2
-1.0
-0.5
0.0
0.5
x
y
TreatedControl
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Using GAMs
-2 -1 0 1 2
-1.0
-0.5
0.0
0.5
x
y
TreatedControl
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 111 / 176
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Using GAMs
-2 -1 0 1 2
-1.0
-0.5
0.0
0.5
x
y
TreatedControl
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 112 / 176
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Wait...so what are we actually doing most of the time?
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Conclusions
Regression is mechanically very simple, but philosophically somewhatcomplicated
It is a useful descriptive tool for approximating a conditionalexpectation function
Once again though, the estimand of interest isn’t necessarily theregression coefficient.
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Conclusions
Regression is mechanically very simple, but philosophically somewhatcomplicated
It is a useful descriptive tool for approximating a conditionalexpectation function
Once again though, the estimand of interest isn’t necessarily theregression coefficient.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 114 / 176
![Page 468: Week 10: Causality with Measured Confounding · Week 10: Causality with Measured Confounding Brandon Stewart1 Princeton November 28 and 30, 2016 1These slides are heavily in uenced](https://reader035.fdocuments.in/reader035/viewer/2022063023/5ffee30233781c57465bb2fc/html5/thumbnails/468.jpg)
Conclusions
Regression is mechanically very simple, but philosophically somewhatcomplicated
It is a useful descriptive tool for approximating a conditionalexpectation function
Once again though, the estimand of interest isn’t necessarily theregression coefficient.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 114 / 176
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Next Week
Causality with Unmeasured Confounding
Reading:I Fox Chapter 9.8 Instrumental Variables and TSLSI Angrist and Pishke Chapter 4 Instrumental VariablesI Morgan and Winship Chapter 9 Instrumental Variable Estimators of
Causal EffectsI Optional: Hernan and Robins Chapter 16 Instrumental Variable
EstimationI Optional: Sovey, Allison J. and Green, Donald P. 2011. “Instrumental
Variables Estimation in Political Science: A Readers’ Guide.” AmericanJournal of Political Science
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 116 / 176
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Visualization in the New York Times
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Visualization in the New York Times
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Visualization in the New York Times
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Visualization in the New York Times
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Alternate Graphs
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Alternate Graphs
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Alternate Graphs
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Alternate Graphs
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Alternate Graphs
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Alternate Graphs
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Thoughts
Two stories here:
1 Visualization and data coding choices are important
2 The internet is amazing (especially with replication data beingavailable!)
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Thoughts
Two stories here:
1 Visualization and data coding choices are important
2 The internet is amazing (especially with replication data beingavailable!)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 119 / 176
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Thoughts
Two stories here:
1 Visualization and data coding choices are important
2 The internet is amazing (especially with replication data beingavailable!)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 119 / 176
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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This Appendix
The main lecture slides have glossed over some of the details andassumptions for identification
This appendix contains mathematical results and conditions necessaryto estimate causal effects.
I have also included a section with more details on blocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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Subclassification Estimator
Identification Result
τATE =
∫ (E[Y |X ,D = 1]− E[Y |X ,D = 0]
)dP(X )
τATT =
∫ (E[Y |X ,D = 1]− E[Y |X ,D = 0]
)dP(X |D = 1)
Assume X takes on K different cells X 1, ...,X k , ...,XK. Then theanalogy principle suggests estimators:
τATE =K∑
k=1
(Y k
1 − Y k0
)·(Nk
N
); τATT =
K∑k=1
(Y k
1 − Y k0
)·(Nk
1
N1
)Nk is # of obs. and Nk
1 is # of treated obs. in cell k
Y k1 is mean outcome for the treated in cell k
Y k0 is mean outcome for the untreated in cell k
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Subclassification Estimator
Identification Result
τATE =
∫ (E[Y |X ,D = 1]− E[Y |X ,D = 0]
)dP(X )
τATT =
∫ (E[Y |X ,D = 1]− E[Y |X ,D = 0]
)dP(X |D = 1)
Assume X takes on K different cells X 1, ...,X k , ...,XK. Then theanalogy principle suggests estimators:
τATE =K∑
k=1
(Y k
1 − Y k0
)·(Nk
N
); τATT =
K∑k=1
(Y k
1 − Y k0
)·(Nk
1
N1
)Nk is # of obs. and Nk
1 is # of treated obs. in cell k
Y k1 is mean outcome for the treated in cell k
Y k0 is mean outcome for the untreated in cell k
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 123 / 176
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Subclassification by Age (K = 2)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old 28 24 4 3 10
Young 22 16 6 7 10
Total 10 20
What is τATE =∑K
k=1
(Y k
1 − Y k0
)·(Nk
N
)?
τATE = 4 · (10/20) + 6 · (10/20) = 5
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Subclassification by Age (K = 2)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old 28 24 4 3 10
Young 22 16 6 7 10
Total 10 20
What is τATE =∑K
k=1
(Y k
1 − Y k0
)·(Nk
N
)?
τATE = 4 · (10/20) + 6 · (10/20) = 5
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Subclassification by Age (K = 2)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old 28 24 4 3 10
Young 22 16 6 7 10
Total 10 20
What is τATT =∑K
k=1
(Y k
1 − Y k0
)·(Nk
1N1
)?
τATT = 4 · (3/10) + 6 · (7/10) = 5.4
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Subclassification by Age (K = 2)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old 28 24 4 3 10
Young 22 16 6 7 10
Total 10 20
What is τATT =∑K
k=1
(Y k
1 − Y k0
)·(Nk
1N1
)?
τATT = 4 · (3/10) + 6 · (7/10) = 5.4
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Subclassification by Age and Gender (K = 4)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old, Male 28 22 4 3 7
Old, Female 24 0 3
Young, Male 21 16 5 3 4
Young, Female 23 17 6 4 6
Total 10 20
What is τATE =∑K
k=1
(Y k
1 − Y k0
)·(Nk
N
)?
Not identified!
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Subclassification by Age and Gender (K = 4)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old, Male 28 22 4 3 7
Old, Female 24 0 3
Young, Male 21 16 5 3 4
Young, Female 23 17 6 4 6
Total 10 20
What is τATE =∑K
k=1
(Y k
1 − Y k0
)·(Nk
N
)?
Not identified!
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Subclassification by Age and Gender (K = 4)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old, Male 28 22 4 3 7
Old, Female 24 0 3
Young, Male 21 16 5 3 4
Young, Female 23 17 6 4 6
Total 10 20
What is τATT =∑K
k=1
(Y k
1 − Y k0
)·(Nk
1N1
)?
τATT = 4 · (3/10) + 5 · (3/10) + 6 · (4/10) = 5.1
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Subclassification by Age and Gender (K = 4)
Death Rate Death Rate # #Xk Smokers Non-Smokers Diff. Smokers Obs.
Old, Male 28 22 4 3 7
Old, Female 24 0 3
Young, Male 21 16 5 3 4
Young, Female 23 17 6 4 6
Total 10 20
What is τATT =∑K
k=1
(Y k
1 − Y k0
)·(Nk
1N1
)?
τATT = 4 · (3/10) + 5 · (3/10) + 6 · (4/10) = 5.1
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 127 / 176
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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Selection Bias
Recall the selection problem when comparing the mean outcomes for thetreated and the untreated:
Problem
E[Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
= E[Y1|D = 1]− E[Y0|D = 0]
= E[Y1 − Y0|D = 1]︸ ︷︷ ︸ATT
+ E[Y0|D = 1]− E[Y0|D = 0]︸ ︷︷ ︸BIAS
How can we eliminate the bias term?
As a result of randomization, the selection bias term will be zero
The treatment and control group will tend to be similar along allcharacteristics (identical in expectation), including the potentialoutcomes under the control condition
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Selection Bias
Recall the selection problem when comparing the mean outcomes for thetreated and the untreated:
Problem
E[Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
= E[Y1|D = 1]− E[Y0|D = 0]
= E[Y1 − Y0|D = 1]︸ ︷︷ ︸ATT
+ E[Y0|D = 1]− E[Y0|D = 0]︸ ︷︷ ︸BIAS
How can we eliminate the bias term?
As a result of randomization, the selection bias term will be zero
The treatment and control group will tend to be similar along allcharacteristics (identical in expectation), including the potentialoutcomes under the control condition
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 129 / 176
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Identification Under Random Assignment
Identification Assumption
(Y1,Y0)⊥⊥D (random assignment)
Identification Result
Problem: τATE = E[Y1 − Y0] is unobserved. But given random assignment
E[Y |D = 1] = E[D · Y1 + (1− D) · Y0|D = 1]
= E[Y1|D = 1]
= E[Y1]
E[Y |D = 0] = E[D · Y1 + (1− D) · Y0|D = 0]
= E[Y0|D = 0]
= E[Y0]
τATE = E[Y1 − Y0] = E[Y1]− E[Y0] = E[Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
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Identification Under Random Assignment
Identification Assumption
(Y1,Y0)⊥⊥D (random assignment)
Identification Result
Problem: τATE = E[Y1 − Y0] is unobserved. But given random assignment
E[Y |D = 1] = E[D · Y1 + (1− D) · Y0|D = 1]
= E[Y1|D = 1]
= E[Y1]
E[Y |D = 0] = E[D · Y1 + (1− D) · Y0|D = 0]
= E[Y0|D = 0]
= E[Y0]
τATE = E[Y1 − Y0] = E[Y1]− E[Y0] = E[Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
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Identification Under Random Assignment
Identification Assumption
(Y1,Y0)⊥⊥D (random assignment)
Identification Result
Problem: τATE = E[Y1 − Y0] is unobserved. But given random assignment
E[Y |D = 1] = E[D · Y1 + (1− D) · Y0|D = 1]
= E[Y1|D = 1]
= E[Y1]
E[Y |D = 0] =
E[D · Y1 + (1− D) · Y0|D = 0]
= E[Y0|D = 0]
= E[Y0]
τATE = E[Y1 − Y0] = E[Y1]− E[Y0] = E[Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
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Identification Under Random Assignment
Identification Assumption
(Y1,Y0)⊥⊥D (random assignment)
Identification Result
Problem: τATE = E[Y1 − Y0] is unobserved. But given random assignment
E[Y |D = 1] = E[D · Y1 + (1− D) · Y0|D = 1]
= E[Y1|D = 1]
= E[Y1]
E[Y |D = 0] = E[D · Y1 + (1− D) · Y0|D = 0]
= E[Y0|D = 0]
= E[Y0]
τATE = E[Y1 − Y0] = E[Y1]− E[Y0] = E[Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
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Average Treatment Effect (ATE)Imagine a population with 4 units:
i Y1i Y0i Yi Di
1 3 0 3 12 1 1 1 13 2 0 0 04 2 1 1 0
What is τATE = E[Y1]− E[Y0]?
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Average Treatment Effect (ATE)Imagine a population with 4 units:
i Y1i Y0i Yi Di
1 3 0 3 12 1 1 1 13 2 0 0 04 2 1 1 0
E[Y1] 2E[Y0] .5
τATE = E[Y1]− E[Y0] = 2− .5 = 1.5
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Average Treatment Effect (ATE)Imagine a population with 4 units:
i Y1i Y0i Yi Di
1 3 ? 3 12 1 ? 1 13 ? 0 0 04 ? 1 1 0
E[Y1] ?E[Y0] ?
What is τATE = E[Y1]− E[Y0]?
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Average Treatment Effect (ATE)Imagine a population with 4 units:
i Y1i Y0i Yi Di P(Di = 1)1 3 ? 3 1 ?2 1 ? 1 1 ?3 ? 0 0 0 ?4 ? 1 1 0 ?
E[Y1] ?E[Y0] ?
What is τATE = E[Y1]−E[Y0]? In an experiment, the researcher controls the prob-ability of assignment to treatment for all units P(Di = 1) and by imposing equalprobabilities we ensure that treatment assignment is independent of the potentialoutcomes, i.e. (Y1,Y0)⊥⊥D.
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Average Treatment Effect (ATE)Imagine a population with 4 units:
i Y1i Y0i Yi Di P(Di = 1)1 3 0 3 1 2/42 1 1 1 1 2/43 2 0 0 0 2/44 2 1 1 0 2/4
E[Y1] 2E[Y0] .5
What is τATE = E[Y1]−E[Y0]? Given that Di is randomly assigned with probability1/2, we have E[Y |D = 1] = E[Y1|D = 1] = E[Y1].
All possible randomizations with two treated units:
Treated Units: 1 & 2 1 & 3 1 & 4 2 & 3 2 & 4 3 & 4Average Y |D = 1: 2 2.5 2.5 1.5 1.5 2
So E[Y |D = 1] = E[Y1] = 2
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Average Treatment Effect (ATE)Imagine a population with 4 units:
i Y1i Y0i Yi Di P(Di = 1)1 3 0 3 1 2/42 1 1 1 1 2/43 2 0 0 0 2/44 2 1 1 0 2/4
E[Y1] 2E[Y0] .5
By the same logic, we have: E[Y |D = 0] = E[Y0|D = 0] = E[Y0] = .5.
Therefore the average treatment effect is identified:
τATE = E[Y1]− E[Y0] = E[Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
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Average Treatment Effect (ATE)Imagine a population with 4 units:
i Y1i Y0i Yi Di P(Di = 1)1 3 0 3 1 2/42 1 1 1 1 2/43 2 0 0 0 2/44 2 1 1 0 2/4
E[Y1] 2E[Y0] .5
Also since E[Y |D = 0] = E[Y0|D = 0] = E[Y0|D = 1] = E[Y0]we have that
τATT = E[Y1 − Y0|D = 1] = E[Y1|D = 1]− E[Y0|D = 0]
= E[Y1]− E[Y0] = E[Y1 − Y0]
= τATE
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Identification under Random Assignment
Identification Assumption(Y1,Y0)⊥⊥D (random assignment)
Identification ResultWe have that
E[Y0|D = 0] = E[Y0] = E[Y0|D = 1]
and therefore
E [Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
= E [Y1 − Y0|D = 1]︸ ︷︷ ︸ATET
+ E[Y0|D = 1]− E[Y0|D = 0]︸ ︷︷ ︸BIAS
= E [Y1 − Y0|D = 1]︸ ︷︷ ︸ATET
As a result,
E [Y |D = 1]− E[Y |D = 0]︸ ︷︷ ︸Difference in Means
= τATE = τATET
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Identification in Randomized Experiments
Identification Assumption
Given random assignment (Y1,Y0)⊥⊥D
Identification Result
Let FYd(y) be the cumulative distribution function (CDF) of Yd , then
FY0 (y) = Pr(Y0 ≤ y) = Pr(Y0 ≤ y |D = 0)
= Pr(Y ≤ y |D = 0).
Similarly,FY1 (y) = Pr(Y ≤ y |D = 1).
So the effect of the treatment at any quantile θ ∈ [0, 1] is identified:
αθ = Qθ(Y1)− Qθ(Y0) = Qθ(Y |D = 1)− Qθ(Y |D = 0)
where FYd(Qθ(Yd)) = θ.
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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1 The Experimental Ideal
2 Assumption of No Unmeasured Confounding
3 Fun With Censorship
4 Regression Estimators
5 Agnostic Regression
6 Regression and Causality
7 Regression Under Heterogeneous Effects
8 Fun with Visualization, Replication and the NYT
9 AppendixSubclassificationIdentification under Random AssignmentEstimation Under Random AssignmentBlocking
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Estimation Under Random AssignmentConsider a randomized trial with N individuals.
Estimand
τATE = E[Y1 − Y0] = E[Y |D = 1]− E[Y |D = 0]
Estimator
By the analogy principle we use
τ = Y1 − Y0
Y1 =
∑Yi · Di∑Di
=1
N1
∑Di=1
Yi ;
Y0 =
∑Yi · (1− Di )∑
(1− Di )=
1
N0
∑Di=0
Yi
with N1 =∑
i Di and N0 = N − N1.
Under random assignment, τ is an unbiased and consistent estimator of τATE(E[τ ] = τATE and τN
p→ τATE .)
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Estimation Under Random AssignmentConsider a randomized trial with N individuals.
Estimand
τATE = E[Y1 − Y0] = E[Y |D = 1]− E[Y |D = 0]
EstimatorBy the analogy principle we use
τ = Y1 − Y0
Y1 =
∑Yi · Di∑Di
=1
N1
∑Di=1
Yi ;
Y0 =
∑Yi · (1− Di )∑
(1− Di )=
1
N0
∑Di=0
Yi
with N1 =∑
i Di and N0 = N − N1.
Under random assignment, τ is an unbiased and consistent estimator of τATE(E[τ ] = τATE and τN
p→ τATE .)
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Unbiasedness Under Random Assignment
One way of showing that τ is unbiased is to exploit the fact that underindependence of potential outcomes and treatment status, E[D] = N1
N and
E[1− D] = N0N
Rewrite the estimators as follows:
τ =1
N
N∑i=1
(D · Y1
N1/N− (1− D) · Y0
N0/N
)Take expectations with respect to the sampling distribution given by thedesign. Under the Neyman model, Y1 and Y0 are fixed and only Di israndom.
E[τ ] =1
N
N∑i=1
(E[D] · Y1
N1/N− E[(1− D)] · Y0
N0/N
)=
1
N
N∑i=1
(Y1 − Y0) = τ
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Unbiasedness Under Random Assignment
One way of showing that τ is unbiased is to exploit the fact that underindependence of potential outcomes and treatment status, E[D] = N1
N and
E[1− D] = N0N
Rewrite the estimators as follows:
τ =1
N
N∑i=1
(D · Y1
N1/N− (1− D) · Y0
N0/N
)Take expectations with respect to the sampling distribution given by thedesign. Under the Neyman model, Y1 and Y0 are fixed and only Di israndom.
E[τ ] =1
N
N∑i=1
(E[D] · Y1
N1/N− E[(1− D)] · Y0
N0/N
)=
1
N
N∑i=1
(Y1 − Y0) = τ
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Unbiasedness Under Random Assignment
One way of showing that τ is unbiased is to exploit the fact that underindependence of potential outcomes and treatment status, E[D] = N1
N and
E[1− D] = N0N
Rewrite the estimators as follows:
τ =1
N
N∑i=1
(D · Y1
N1/N− (1− D) · Y0
N0/N
)Take expectations with respect to the sampling distribution given by thedesign. Under the Neyman model, Y1 and Y0 are fixed and only Di israndom.
E[τ ] =1
N
N∑i=1
(E[D] · Y1
N1/N− E[(1− D)] · Y0
N0/N
)=
1
N
N∑i=1
(Y1 − Y0) = τ
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What is the Estimand?
So far we have emphasized effect estimation, but what aboutuncertainty?
In the design based literature, variability in our estimates can arisefrom two sources:
1 Sampling variation induced by the procedure that selected the unitsinto our sample.
2 Variation induced by the particular realization of the treatment variable.
This distinction is important, but often ignored
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What is the Estimand?
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0 Y1 Y0 Y1 Y0Y1 Y0 Y1 Y0 Y1 Y0 Y1 Y0 Y1 Y0 Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0Y1 Y0
Population Sample
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SATE and PATE
Typically we focus on estimating the average causal effect in aparticular sample: Sample Average Treatment Effect (SATE)
I Uncertainty arises only from hypothetical randomizations.
I Inferences are limited to the sample in our study.
Might care about the Population Average Treatment Effect (PATE)
I Requires precise knowledge about the sampling process that selectedunits from the population into the sample.
I Need to account for two sources of variation:
F Variation from the sampling process
F Variation from treatment assignment.
Thus, in general, Var(PATE) > Var(SATE).
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Standard Error for Sample ATE
The standard error is the standard deviation of a sampling distribution:
SEθ ≡√
1J
∑J1(θj − θ)2 (with J possible random assignments).
i Y1i Y0i Yi Di P(Di = 1)1 3 0 3 1 2/42 1 1 1 1 2/43 2 0 0 0 2/44 2 1 1 0 2/4
ATE estimates given all possible random assignments with two treated units:
Treated Units: 1 & 2 1 & 3 1 & 4 2 & 3 2 & 4 3 & 4
ATE : 1.5 1.5 2 1 1.5 1.5
The average ATE is 1.5 and therefore the true standard error isSE
ATE=
√16
[(1.5− 1.5)2 + (1.5− 1.5)2 + (2− 1.5)2 + (1− 1.5)2 + (1.5− 1.5)2 + (1.5− 1.5)2] ≈ .28
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Standard Error for Sample ATE
Standard Error for Sample ATEGiven complete randomization of N units with N1 assigned to treatment and N0 = N − N1 tocontrol, the true standard error of the estimated sample ATE is given by
SEATE
=
√(N − N1
N − 1
)Var [Y1i ]
N1+
(N − N0
N − 1
)Var [Y0i ]
N0+
(1
N − 1
)2Cov [Y1i ,Y0i ]
with population variances and covariance
Var [Ydi ] ≡1
N
N∑1
(Ydi −
∑N1 Ydi
N
)2
= σ2Yd |Di=d
Cov [Y1i ,Y0i ] ≡1
N
N∑1
(Y1i −
∑N1 Y1i
N
)(Y0i −
∑N1 Y0i
N
)= σ2
Y1,Y0
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Standard Error for Sample ATE
Standard Error for Sample ATEGiven complete randomization of N units with N1 assigned to treatment and N0 = N − N1 tocontrol, the true standard error of the estimated sample ATE is given by
SEATE
=
√(N − N1
N − 1
)Var [Y1i ]
N1+
(N − N0
N − 1
)Var [Y0i ]
N0+
(1
N − 1
)2Cov [Y1i ,Y0i ]
with population variances and covariance
Var [Ydi ] ≡1
N
N∑1
(Ydi −
∑N1 Ydi
N
)2
= σ2Yd |Di=d
Cov [Y1i ,Y0i ] ≡1
N
N∑1
(Y1i −
∑N1 Y1i
N
)(Y0i −
∑N1 Y0i
N
)= σ2
Y1,Y0
Plugging in, we obtain the true standard error of the estimated sample ATE
SEATE
=
√(4− 2
4− 1
).25
2+
(4− 2
4− 1
).5
2+
(1
4− 1
)2(−.25) ≈ .28
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Standard Error for Sample ATE
Standard Error for Sample ATEGiven complete randomization of N units with N1 assigned to treatment and N0 = N − N1 tocontrol, the true standard error of the estimated sample ATE is given by
SEATE
=
√(N − N1
N − 1
)Var [Y1i ]
N1+
(N − N0
N − 1
)Var [Y0i ]
N0+
(1
N − 1
)2Cov [Y1i ,Y0i ]
with population variances and covariance
Var [Ydi ] ≡1
N
N∑1
(Ydi −
∑N1 Ydi
N
)2
= σ2Yd |Di=d
Cov [Y1i ,Y0i ] ≡1
N
N∑1
(Y1i −
∑N1 Y1i
N
)(Y0i −
∑N1 Y0i
N
)= σ2
Y1,Y0
Standard error decreases if:
N grows
Var [Y1], Var [Y0] decrease
Cov [Y1,Y0] decreases
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Conservative Estimator SEATE
Conservative Estimator for Standard Error for Sample ATE
SEATE
=
√Var [Y1i ]
N1+
Var [Y0i ]
N0
with estimators of the sample variances given by
Var [Y1i ] ≡1
N1 − 1
N∑i|Di=1
(Y1i −
∑Ni|Di=1 Y1i
N1
)2
= σ2Y |Di=1
Var [Y0i ] ≡1
N0 − 1
N∑i|Di=0
(Y0i −
∑Ni|Di=0 Y0i
N0
)2
= σ2Y |Di=0
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Conservative Estimator SEATE
Conservative Estimator for Standard Error for Sample ATE
SEATE
=
√Var [Y1i ]
N1+
Var [Y0i ]
N0
with estimators of the sample variances given by
Var [Y1i ] ≡1
N1 − 1
N∑i|Di=1
(Y1i −
∑Ni|Di=1 Y1i
N1
)2
= σ2Y |Di=1
Var [Y0i ] ≡1
N0 − 1
N∑i|Di=0
(Y0i −
∑Ni|Di=0 Y0i
N0
)2
= σ2Y |Di=0
What about the covariance?
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Conservative Estimator SEATE
Conservative Estimator for Standard Error for Sample ATE
SEATE
=
√Var [Y1i ]
N1+
Var [Y0i ]
N0
with estimators of the sample variances given by
Var [Y1i ] ≡1
N1 − 1
N∑i|Di=1
(Y1i −
∑Ni|Di=1 Y1i
N1
)2
= σ2Y |Di=1
Var [Y0i ] ≡1
N0 − 1
N∑i|Di=0
(Y0i −
∑Ni|Di=0 Y0i
N0
)2
= σ2Y |Di=0
Conservative compared to the true standard error, i.e. SEATE
< SEATE
Asymptotically unbiased in two special cases:
if τi is constant (i.e. Cor [Y1,Y0] = 1)
if we estimate standard error of population average treatment effect (Cov [Y1,Y0] isnegligible when we sample from a large population)
Equivalent to standard error for two sample t-test with unequal variances or “robust”standard error in regression of Y on D
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Proof: SEATE≤ SE
ATEUpper bound for standard error is when Cor [Y1,Y0] = 1:
Cor [Y1,Y0] =Cov [Y1,Y0]√Var [Y1]Var [Y0]
≤ 1⇐⇒ Cov [Y1,Y0] ≤√
Var [Y1]Var [Y0]
SEATE
=
√(N − N1
N − 1
)Var [Y1]
N1+
(N − N0
N − 1
)Var [Y0]
N0+
(1
N − 1
)2Cov [Y1,Y0]
=
√1
N − 1
(N0
N1Var [Y1] +
N1
N0Var [Y0] + 2Cov [Y1,Y0]
)
≤
√1
N − 1
(N0
N1Var [Y1] +
N1
N0Var [Y0] + 2
√Var [Y1]Var [Y0]
)
≤
√1
N − 1
(N0
N1Var [Y1] +
N1
N0Var [Y0] + Var [Y1] + Var [Y0]
)
Last step follows from the following inequality
(√
Var [Y1]−√
Var [Y0])2 ≥ 0
Var [Y1]− 2√
Var [Y1]Var [Y0] + Var [Y0] ≥ 0⇐⇒ Var [Y1] + Var [Y0] ≥ 2√
Var [Y1]Var [Y0]
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Proof: SEATE≤ SE
ATE
SEATE
≤
√1
N − 1
(N0
N1Var [Y1] +
N1
N0Var [Y0] + Var [Y1] + Var [Y0]
)
≤
√N2
0Var [Y1] + N21Var [Y0] + N1N0(Var [Y1] + Var [Y0])
(N − 1)N1N0
≤
√(N2
0 + N1N0)Var [Y1] + (N21 + N1N0)Var [Y0]
(N − 1)N1N0
≤
√(N0 + N1)N0Var [Y1]
(N − 1)N1N0+
(N1 + N0)N1Var [Y0]
(N − 1)N1N0
≤
√N Var [Y1]
(N − 1)N1+
N Var [Y0]
(N − 1)N0
≤
√N
N − 1
(Var [Y1]
N1+
Var [Y0]
(N0)
)
≤
√√√√ N
N − 1
(Var [Y1]
N1+
Var [Y0]
(N0)
)
So the estimator for the standard error is conservative.Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 144 / 176
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Standard Error for Sample ATE
i Y1i Y0i Yi
1 3 0 32 1 1 13 2 0 04 2 1 1
SEATE
estimates given all possible assignments with two treated units:
Treated Units: 1 & 2 1 & 3 1 & 4 2 & 3 2 & 4 3 & 4
ATE : 1.5 1.5 2 1 1.5 1.5
SEATE
: 1.11 .5 .71 .71 .5 .5
The average SEATE
is ≈ .67 compared to the true standard error of SEATE≈ .28
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Example: Effect of Training on Earnings
Treatment Group:
I N1 = 7, 487I Estimated Average Earnings Y1: $16, 199I Estimated Sample Standard deviation σY |Di=1: $17, 038
Control Group :
I N0 = 3, 717I Estimated Average Earnings Y0: $15, 040I Estimated Sample deviation σY |Di=0: $16, 180
Estimated average effect of training:
I τATE = Y1 − Y0 = 16, 199− 15, 040 = $1, 159
Estimated standard error for effect of training:
I SEATE
=
√σ2Y |Di =1
N1+
σ2Y |Di =0
(N0) =√
17,0382
7,487 + 16,1802
3,717 ≈ $330
Is this consistent with a zero average treatment effect αATE = 0?
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Example: Effect of Training on Earnings
Treatment Group:
I N1 = 7, 487I Estimated Average Earnings Y1: $16, 199I Estimated Sample Standard deviation σY |Di=1: $17, 038
Control Group :
I N0 = 3, 717I Estimated Average Earnings Y0: $15, 040I Estimated Sample deviation σY |Di=0: $16, 180
Estimated average effect of training:
I τATE = Y1 − Y0 = 16, 199− 15, 040 = $1, 159
Estimated standard error for effect of training:
I SEATE
=
√σ2Y |Di =1
N1+
σ2Y |Di =0
(N0) =√
17,0382
7,487 + 16,1802
3,717 ≈ $330
Is this consistent with a zero average treatment effect αATE = 0?
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Example: Effect of Training on Earnings
Treatment Group:
I N1 = 7, 487I Estimated Average Earnings Y1: $16, 199I Estimated Sample Standard deviation σY |Di=1: $17, 038
Control Group :
I N0 = 3, 717I Estimated Average Earnings Y0: $15, 040I Estimated Sample deviation σY |Di=0: $16, 180
Estimated average effect of training:
I τATE = Y1 − Y0 = 16, 199− 15, 040 = $1, 159
Estimated standard error for effect of training:
I SEATE
=
√σ2Y |Di =1
N1+
σ2Y |Di =0
(N0) =√
17,0382
7,487 + 16,1802
3,717 ≈ $330
Is this consistent with a zero average treatment effect αATE = 0?
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Testing the Null Hypothesis of Zero Average Effect
Under the null hypothesis H0: τATE = 0, the average potential outcomes inthe population are the same for treatment and control: E[Y1] = E[Y0].
Since units are randomly assigned, both the treatment and control groupsshould therefore have the same sample average earnings
However, we in fact observe a difference in mean earnings of $1, 159
What is the probability of observing a difference this large if the true averageeffect of the training were zero (i.e. the null hypothesis were true)?
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Testing the Null Hypothesis of Zero Average Effect
Use a two-sample t-test with unequal variances:
t =τ√
σ2Yi |Di=1
N1+σ2Yi |Di=0
N0
=$1, 159√
$17, 0382
7, 487+
$16, 1802
3, 717
≈ 3.5
I From basic statistical theory, we know that tNd→ N (0, 1)
I And for a standard normal distribution, the probability of observing avalue of t that is larger than |t| > 1.96 is < .05
I So obtaining a value as high as t = 3.5 is very unlikely under the nullhypothesis of a zero average effect
I We reject the null hypothesis H0: τ0 = 0 against the alternative H1:τ0 6= 0 at asymptotic 5% significance level whenever |t| > 1.96.
I Inverting the test statistic we can construct a 95% confidence interval
τATE ± 1.96 · SEATE
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Testing the Null Hypothesis of Zero Average EffectR Code
> d <- read.dta("jtpa.dta")
> head(d[,c("earnings","assignmt")])
earnings assignmt
1 1353 1
2 4984 1
3 27707 1
4 31860 1
5 26615 0
>
> meanAsd <- function(x)+ out <- c(mean(x),sd(x))
+ names(out) <- c("mean","sd")
+ return(out)
+ >
> aggregate(earnings~assignmt,data=d,meanAsd)
assignmt earnings.mean earnings.sd
1 0 15040.50 16180.25
2 1 16199.94 17038.85
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Testing the Null Hypothesis of Zero Average Effect
R Code> t.test(earnings~assignmt,data=d,var.equal=FALSE)
Welch Two Sample t-test
data: earnings by assignmt
t = -3.5084, df = 7765.599, p-value = 0.0004533
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-1807.2427 -511.6239
sample estimates:
mean in group 0 mean in group 1
15040.50 16199.94
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Regression to Estimate the Average Treatment Effect
Estimator (Regression)
The ATE can be expressed as a regression equation:
Yi = Di Y1i + (1− Di )Y0i
= Y0i + (Y1i − Y0i )Di
= Y0︸︷︷︸α
+ (Y1 − Y0)︸ ︷︷ ︸τReg
Di + (Yi0 − Y0) + Di · [(Yi1 − Y1)− (Yi0 − Y0)]︸ ︷︷ ︸ε
= α + τRegDi + εiτReg could be biased for τATE in two ways:
I Baseline difference in potential outcomes under control that iscorrelated with Di .
I Individual treatment effects τi are correlated with Di
I Under random assignment, both correlations are zero in expectation
Effect heterogeneity implies “heteroskedasticity”, i.e. error variance differsby values of Di .
I Neyman model imples “robust” standard errors.
Can use regression in experiments without assuming constant effects.
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Regression to Estimate the Average Treatment Effect
Estimator (Regression)
The ATE can be expressed as a regression equation:
Yi = Di Y1i + (1− Di )Y0i
= Y0i + (Y1i − Y0i )Di
= Y0︸︷︷︸α
+ (Y1 − Y0)︸ ︷︷ ︸τReg
Di + (Yi0 − Y0) + Di · [(Yi1 − Y1)− (Yi0 − Y0)]︸ ︷︷ ︸ε
= α + τRegDi + εiτReg could be biased for τATE in two ways:
I Baseline difference in potential outcomes under control that iscorrelated with Di .
I Individual treatment effects τi are correlated with Di
I Under random assignment, both correlations are zero in expectation
Effect heterogeneity implies “heteroskedasticity”, i.e. error variance differsby values of Di .
I Neyman model imples “robust” standard errors.
Can use regression in experiments without assuming constant effects.
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Regression to Estimate the Average Treatment Effect
Estimator (Regression)
The ATE can be expressed as a regression equation:
Yi = Di Y1i + (1− Di )Y0i
= Y0i + (Y1i − Y0i )Di
= Y0︸︷︷︸α
+ (Y1 − Y0)︸ ︷︷ ︸τReg
Di + (Yi0 − Y0) + Di · [(Yi1 − Y1)− (Yi0 − Y0)]︸ ︷︷ ︸ε
= α + τRegDi + εiτReg could be biased for τATE in two ways:
I Baseline difference in potential outcomes under control that iscorrelated with Di .
I Individual treatment effects τi are correlated with Di
I Under random assignment, both correlations are zero in expectation
Effect heterogeneity implies “heteroskedasticity”, i.e. error variance differsby values of Di .
I Neyman model imples “robust” standard errors.
Can use regression in experiments without assuming constant effects.Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 151 / 176
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Regression to Estimate the Average Treatment Effect
R Code> library(sandwich)
> library(lmtest)
>
> lout <- lm(earnings~assignmt,data=d)
> coeftest(lout,vcov = vcovHC(lout, type = "HC1")) # matches Stata
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15040.50 265.38 56.6752 < 2.2e-16 ***
assignmt 1159.43 330.46 3.5085 0.0004524 ***
---
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Covariates and Experiments
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0
Y1 Y0 Y1 Y0 Y1 Y0Y1 Y0 Y1 Y0 Y1 Y0 Y1 Y0 Y1 Y0 Y1 Y0
X
X
X
X
XX
X X X X X X
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Covariates
Randomization is gold standard for causal inference because inexpectation it balances observed but also unobserved characteristicsbetween treatment and control group.
Unlike potential outcomes, you observe baseline covariates for allunits. Covariate values are predetermined with respect to thetreatment and do not depend on Di .
Under randomization, fX |D(X |D = 1)d= fX |D(X |D = 0) (equality in
distribution).
Similarity in distributions of covariates is known as covariate balance.
If this is not the case, then one of two possibilities:
I Randomization was compromised.I Sampling error (bad luck)
One should always test for covariate balance on important covariates,using so called “balance checks” (eg. t-tests, F-tests, etc.)
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Covariates
Randomization is gold standard for causal inference because inexpectation it balances observed but also unobserved characteristicsbetween treatment and control group.
Unlike potential outcomes, you observe baseline covariates for allunits. Covariate values are predetermined with respect to thetreatment and do not depend on Di .
Under randomization, fX |D(X |D = 1)d= fX |D(X |D = 0) (equality in
distribution).
Similarity in distributions of covariates is known as covariate balance.
If this is not the case, then one of two possibilities:I Randomization was compromised.I Sampling error (bad luck)
One should always test for covariate balance on important covariates,using so called “balance checks” (eg. t-tests, F-tests, etc.)
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Covariates and Experiments
5
10
15
−3 −2 −1 0 1 2Covariate
Out
com
e
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Covariates and Experiments
0
100
200
300
400
−1.0 −0.5 0.0 0.5 1.0Covariate Imbalance
coun
t
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Regression with Covariates
Practioners often run some variant of the following model withexperimental data:
Yi = α + τDi + Xiβ + εi
Why include Xi when experiments “control” for covariates by design?
I Correct for chance covariate imbalances that indicate that τ may be farfrom τATE .
I Increase precision: remove variation in the outcome accounted for bypre-treatment characteristics, thus making it easier to attributeremaining differences to the treatment.
ATE estimates are robust to model specification (with sufficient N).I Never control for post-treatment covariates!
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Regression with Covariates
Practioners often run some variant of the following model withexperimental data:
Yi = α + τDi + Xiβ + εi
Why include Xi when experiments “control” for covariates by design?
I Correct for chance covariate imbalances that indicate that τ may be farfrom τATE .
I Increase precision: remove variation in the outcome accounted for bypre-treatment characteristics, thus making it easier to attributeremaining differences to the treatment.
ATE estimates are robust to model specification (with sufficient N).I Never control for post-treatment covariates!
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Regression with Covariates
Practioners often run some variant of the following model withexperimental data:
Yi = α + τDi + Xiβ + εi
Why include Xi when experiments “control” for covariates by design?
I Correct for chance covariate imbalances that indicate that τ may be farfrom τATE .
I Increase precision: remove variation in the outcome accounted for bypre-treatment characteristics, thus making it easier to attributeremaining differences to the treatment.
ATE estimates are robust to model specification (with sufficient N).I Never control for post-treatment covariates!
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Regression with Covariates
Practioners often run some variant of the following model withexperimental data:
Yi = α + τDi + Xiβ + εi
Why include Xi when experiments “control” for covariates by design?
I Correct for chance covariate imbalances that indicate that τ may be farfrom τATE .
I Increase precision: remove variation in the outcome accounted for bypre-treatment characteristics, thus making it easier to attributeremaining differences to the treatment.
ATE estimates are robust to model specification (with sufficient N).I Never control for post-treatment covariates!
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True ATE
−2 −1 0 1 2
05
1015
20
x
Y
ATE
Y_1Y_0E[Y_1]E[Y_0]
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True ATE and Unadjusted Regression Estimator
−2 −1 0 1 2
05
1015
20
x
Y
ATE
ATE_DiffMeans
Y_1Y_0Y|D=1Y|D=0E[Y_1]E[Y_0]E[Y|D=1]E[Y|D=0]
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Adjusted Regression Estimator
−2 −1 0 1 2
05
1015
20
x
Y
Y_1Y_0Y|D=1Y|D=0E[Y_1]E[Y|D=1]E[X]E[X|D=1]
E[X]<E[X|D=1], so we expect E[Y_1]<E[Y|D=1]
E[Y|D=1]_reg = E[Y|D=1] + Q(E[X] − E[X|D=1])
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Adjusted Regression Estimator
−2 −1 0 1 2
05
1015
20
x
Y
Y_1Y_0Y|D=1Y|D=0E[Y_1]E[Y|D=1]E[X]E[X|D=1]E[Y|D=1]_reg
E[X]<E[X|D=1], so we expect E[Y_1]<E[Y|D=1]
E[Y|D=1]_reg = E[Y|D=1] + Q(E[X] − E[X|D=1])
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Adjusted Regression Estimator
−2 −1 0 1 2
05
1015
20
x
Y
ATE
ATE_DiffMeans
ATE_RegInteract
Y_1Y_0Y|D=1Y|D=0E[Y_1]E[Y_0]E[X]E[X|D=1]E[X|D=0]E[Y|D=1]_regE[Y|D=0]_reg
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Covariate Adjustment with RegressionFreedman (2008) shows that regression of the form:
Yi = α + τregDi + β1Xi + εi
τreg is consistent for ATE and has small sample bias (unless model is true)I bias is on the order of 1/n and diminishes rapidly as N increases
τreg will not necessarily improve precision if model is incorrectI But harmful to precision only if more than 3/4 of units are assigned to
one treatment condition or Cov(Di ,Y1 − Y0) larger than Cov(Di ,Y ).
Lin (2013) shows that regression of the form:
Yi = α + τinteractDi + β1 · (Xi − X ) + β2 · Di · (Xi − X ) + εi
τinteract is consistent for ATE and has the same small sample bias
Cannot hurt asymptotic precision even if model is incorrect and will likelyincrease precision if covariates are predictive of the outcomes.
Results hold for multiple covariates
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Covariate Adjustment with Regression
0.0
0.2
0.4
0.6
0 1 2 3 4Estimated Treatment Effects
dens
ity
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Why are Experimental Findings Robust to AlternativeSpecifications?
Note the following important property of OLS known as theFrisch-Waugh-Lovell (FWL) theorem or Anatomy of Regression:
βk =Cov(Yi , xki )
Var(xki )
where xki is the residual from a regression of xki on all other covariates.
Any multivariate regression coefficient can be expressed as the coefficienton a bivariate regression between the outcome and the regressor, after“partialling out” other variables in the model.Let Di be the residuals after regressing Di on Xi . For experimental data,on average, what will Di be equal to?Since Di ≈ Di , multivariate regressions will yield similar results to bivariateregressions.
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Why are Experimental Findings Robust to AlternativeSpecifications?
Note the following important property of OLS known as theFrisch-Waugh-Lovell (FWL) theorem or Anatomy of Regression:
βk =Cov(Yi , xki )
Var(xki )
where xki is the residual from a regression of xki on all other covariates.Any multivariate regression coefficient can be expressed as the coefficienton a bivariate regression between the outcome and the regressor, after“partialling out” other variables in the model.
Let Di be the residuals after regressing Di on Xi . For experimental data,on average, what will Di be equal to?Since Di ≈ Di , multivariate regressions will yield similar results to bivariateregressions.
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Why are Experimental Findings Robust to AlternativeSpecifications?
Note the following important property of OLS known as theFrisch-Waugh-Lovell (FWL) theorem or Anatomy of Regression:
βk =Cov(Yi , xki )
Var(xki )
where xki is the residual from a regression of xki on all other covariates.Any multivariate regression coefficient can be expressed as the coefficienton a bivariate regression between the outcome and the regressor, after“partialling out” other variables in the model.Let Di be the residuals after regressing Di on Xi . For experimental data,on average, what will Di be equal to?
Since Di ≈ Di , multivariate regressions will yield similar results to bivariateregressions.
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Why are Experimental Findings Robust to AlternativeSpecifications?
Note the following important property of OLS known as theFrisch-Waugh-Lovell (FWL) theorem or Anatomy of Regression:
βk =Cov(Yi , xki )
Var(xki )
where xki is the residual from a regression of xki on all other covariates.Any multivariate regression coefficient can be expressed as the coefficienton a bivariate regression between the outcome and the regressor, after“partialling out” other variables in the model.Let Di be the residuals after regressing Di on Xi . For experimental data,on average, what will Di be equal to?Since Di ≈ Di , multivariate regressions will yield similar results to bivariateregressions.
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Summary: Covariate Adjustment with Regression
One does not need to believe in the classical linear model (linearity andconstant treatment effects) to tolerate or even advocate OLS covariateadjustment in randomized experiments (agnostic view of regression)
Covariate adjustment can buy you power (and thus allows for a smallersample).
Small sample bias might be a concern in small samples, but usuallyswamped by efficiency gains.
Since covariates are controlled for by design, results are typically not modeldependent
Best if covariate adjustment strategy is pre-specified as this rules out fishing.
Always show the unadjusted estimate for transparency.
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Testing in Small Samples: Fisher’s Exact Test
Test of differences in means with large N:
H0 : E[Y1] = E[Y0], H1 : E[Y1] 6= E[Y0] (weak null)
Fisher’s Exact Test with small N:
H0 : Y1 = Y0, H1 : Y1 6= Y0 (sharp null of no effect)
Let Ω be the set of all possible randomization realizations.
We only observe the outcomes, Yi , for one realization of theexperiment. We calculate τ = Y1 − Y0.
Under the sharp null hypothesis, we can compute the value that thedifference in means estimator would have taken under any otherrealization, τ(ω), for ω ∈ Ω.
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Testing in Small Samples: Fisher’s Exact Test
Test of differences in means with large N:
H0 : E[Y1] = E[Y0], H1 : E[Y1] 6= E[Y0] (weak null)
Fisher’s Exact Test with small N:
H0 : Y1 = Y0,
H1 : Y1 6= Y0 (sharp null of no effect)
Let Ω be the set of all possible randomization realizations.
We only observe the outcomes, Yi , for one realization of theexperiment. We calculate τ = Y1 − Y0.
Under the sharp null hypothesis, we can compute the value that thedifference in means estimator would have taken under any otherrealization, τ(ω), for ω ∈ Ω.
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Testing in Small Samples: Fisher’s Exact Test
Test of differences in means with large N:
H0 : E[Y1] = E[Y0], H1 : E[Y1] 6= E[Y0] (weak null)
Fisher’s Exact Test with small N:
H0 : Y1 = Y0, H1 : Y1 6= Y0 (sharp null of no effect)
Let Ω be the set of all possible randomization realizations.
We only observe the outcomes, Yi , for one realization of theexperiment. We calculate τ = Y1 − Y0.
Under the sharp null hypothesis, we can compute the value that thedifference in means estimator would have taken under any otherrealization, τ(ω), for ω ∈ Ω.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 166 / 176
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Testing in Small Samples: Fisher’s Exact Test
Test of differences in means with large N:
H0 : E[Y1] = E[Y0], H1 : E[Y1] 6= E[Y0] (weak null)
Fisher’s Exact Test with small N:
H0 : Y1 = Y0, H1 : Y1 6= Y0 (sharp null of no effect)
Let Ω be the set of all possible randomization realizations.
We only observe the outcomes, Yi , for one realization of theexperiment. We calculate τ = Y1 − Y0.
Under the sharp null hypothesis, we can compute the value that thedifference in means estimator would have taken under any otherrealization, τ(ω), for ω ∈ Ω.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 166 / 176
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Testing in Small Samples: Fisher’s Exact Test
Test of differences in means with large N:
H0 : E[Y1] = E[Y0], H1 : E[Y1] 6= E[Y0] (weak null)
Fisher’s Exact Test with small N:
H0 : Y1 = Y0, H1 : Y1 6= Y0 (sharp null of no effect)
Let Ω be the set of all possible randomization realizations.
We only observe the outcomes, Yi , for one realization of theexperiment. We calculate τ = Y1 − Y0.
Under the sharp null hypothesis, we can compute the value that thedifference in means estimator would have taken under any otherrealization, τ(ω), for ω ∈ Ω.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 166 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di
1 3 ? 12 1 ? 13 ? 0 04 ? 1 0
τATE 1.5
What do we know given the sharp null H0 : Y1 = Y0?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di
1 3 3 12 1 1 13 0 0 04 1 1 0
τATE 1.5τ(ω) 1.5
Given the full schedule of potential outcomes under the sharp null, we cancompute the null distribution of ATEH0 across all possible randomization.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di Di
1 3 3 1 12 1 1 1 03 0 0 0 14 1 1 0 0
τATE 1.5τ(ω) 1.5 0.5
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di Di Di
1 3 3 1 1 12 1 1 1 0 03 0 0 0 1 04 1 1 0 0 1
τATE 1.5τ(ω) 1.5 0.5 1.5
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di Di Di Di
1 3 3 1 1 1 02 1 1 1 0 0 13 0 0 0 1 0 14 1 1 0 0 1 0
τATE 1.5τ(ω) 1.5 0.5 1.5 -1.5
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di Di Di Di Di
1 3 3 1 1 1 0 02 1 1 1 0 0 1 13 0 0 0 1 0 1 04 1 1 0 0 1 0 1
τATE 1.5τ(ω) 1.5 0.5 1.5 -1.5 -.5
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di Di Di Di Di Di
1 3 3 1 1 1 0 0 02 1 1 1 0 0 1 1 03 0 0 0 1 0 1 0 14 1 1 0 0 1 0 1 1
τATE 1.5τ(ω) 1.5 0.5 1.5 -1.5 -.5 -1.5
So Pr(τ(ω) ≥ τATE ) = 2/6 ≈ .33.
Which assumptions are needed?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Testing in Small Samples: Fisher’s Exact Test
i Y1i Y0i Di Di Di Di Di Di
1 3 3 1 1 1 0 0 02 1 1 1 0 0 1 1 03 0 0 0 1 0 1 0 14 1 1 0 0 1 0 1 1
τATE 1.5τ(ω) 1.5 0.5 1.5 -1.5 -.5 -1.5
So Pr(α(ω) ≥ τATE ) = 2/6 ≈ .33.
Which assumptions are needed? None! Randomization as “reasoned basisfor causal inference” (Fisher 1935)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 167 / 176
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Blocking
Imagine you have data on the units that you are about to randomlyassign. Why leave it to “pure” chance to balance the observedcharacteristics?
Idea in blocking is to pre-stratify the sample and then to randomizeseparately within each stratum to ensure that the groups start outwith identical observable characteristics on the blocked factors.
You effectively run a separate experiment within each stratum,randomization will balance the unobserved attributes
Why is this helpful?
I Four subjects with pre-treatment outcomes of 2,2,8,8I Divided evenly into treatment and control groups and treatment effect
is zeroI Simple random assignment will place 2,2 and 8,8 together in the
same treatment or control group 1/3 of the time
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 168 / 176
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Blocking
Imagine you run an experiment where you block on gender. It’s possible tothink about an ATE composed of two seperate block-specific ATEs:
τ =Nf
Nf + Nm· τf +
Nm
Nf + Nm· τm
An unbiased estimator for this quantity will be
τB =Nf
Nf + Nm· τf +
Nm
Nf + Nm· τm
or more generally, if there are J strata or blocks, then
τB =J∑
j=1
Nj
Nτj
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 169 / 176
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Blocking
Imagine you run an experiment where you block on gender. It’s possible tothink about an ATE composed of two seperate block-specific ATEs:
τ =Nf
Nf + Nm· τf +
Nm
Nf + Nm· τm
An unbiased estimator for this quantity will be
τB =Nf
Nf + Nm· τf +
Nm
Nf + Nm· τm
or more generally, if there are J strata or blocks, then
τB =J∑
j=1
Nj
Nτj
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 169 / 176
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Blocking
Imagine you run an experiment where you block on gender. It’s possible tothink about an ATE composed of two seperate block-specific ATEs:
τ =Nf
Nf + Nm· τf +
Nm
Nf + Nm· τm
An unbiased estimator for this quantity will be
τB =Nf
Nf + Nm· τf +
Nm
Nf + Nm· τm
or more generally, if there are J strata or blocks, then
τB =J∑
j=1
Nj
Nτj
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 169 / 176
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Blocking
Because the randomizations in each block are independent, the variance ofthe blocking estimator is simply (Var(aX + bY ) = a2Var(X ) + b2Var(Y )):
Var(τB) =
(Nf
Nf + Nm
)2
Var(τf ) +
(Nm
Nf + Nm
)2
Var(τm)
or more generally
Var(τB) =J∑
j=1
(Nj
N
)2
Var(τj)
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 170 / 176
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Blocking with Regression
When analyzing a blocked randomized experiment with OLS and the probabilityof receiving treatment is equal across blocks, then OLS with block “fixed effects”will result in a valid estimator of the ATE:
yi = τDi +J∑
j=2
βj · Bij + εi
where Bj is a dummy for the j-th block (one omitted as reference category).
If probabilites of treatment, pij = P(Dij = 1), vary by block, then weight eachobservation:
wij =
(1
pij
)Di +
(1
1− pij
)(1− Di )
Why do this? When treatment probabilities vary by block, then OLS will weightblocks by the variance of the treatment variable in each block. Withoutcorrecting for this, OLS will result in biased estimates of ATE!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 171 / 176
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Blocking with Regression
When analyzing a blocked randomized experiment with OLS and the probabilityof receiving treatment is equal across blocks, then OLS with block “fixed effects”will result in a valid estimator of the ATE:
yi = τDi +J∑
j=2
βj · Bij + εi
where Bj is a dummy for the j-th block (one omitted as reference category).
If probabilites of treatment, pij = P(Dij = 1), vary by block, then weight eachobservation:
wij =
(1
pij
)Di +
(1
1− pij
)(1− Di )
Why do this? When treatment probabilities vary by block, then OLS will weightblocks by the variance of the treatment variable in each block. Withoutcorrecting for this, OLS will result in biased estimates of ATE!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 171 / 176
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Blocking with Regression
When analyzing a blocked randomized experiment with OLS and the probabilityof receiving treatment is equal across blocks, then OLS with block “fixed effects”will result in a valid estimator of the ATE:
yi = τDi +J∑
j=2
βj · Bij + εi
where Bj is a dummy for the j-th block (one omitted as reference category).
If probabilites of treatment, pij = P(Dij = 1), vary by block, then weight eachobservation:
wij =
(1
pij
)Di +
(1
1− pij
)(1− Di )
Why do this? When treatment probabilities vary by block, then OLS will weightblocks by the variance of the treatment variable in each block. Withoutcorrecting for this, OLS will result in biased estimates of ATE!
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 171 / 176
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When Does Blocking Help?
Imagine a model for a complete and blocked randomized design:
Yi = α + τCRDi + εi
(1)
Yi = α + τBRDi +J∑
j=2
βjBij + ε∗i (2)
where Bj is a dummy for the j-th block. Then given iid sampling:
Var [τCR ] =σ2ε∑n
i=1(Di − D)2with σ2
ε =
∑ni=1 ε
2i
n − 2=
SSRεn − 2
Var [τBR ] =σ2ε∗∑n
i=1(Di − D)2(1− R2j )
with σε∗2 =
∑ni=1 ε
∗2
i
n − k − 1=
SSRε∗
n − k − 1
where R2j is R2 from regression of D on all Bj variables and a constant.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 172 / 176
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When Does Blocking Help?
Imagine a model for a complete and blocked randomized design:
Yi = α + τCRDi + εi (1)
Yi = α + τBRDi +J∑
j=2
βjBij + ε∗i (2)
where Bj is a dummy for the j-th block. Then given iid sampling:
Var [τCR ] =σ2ε∑n
i=1(Di − D)2with σ2
ε =
∑ni=1 ε
2i
n − 2=
SSRεn − 2
Var [τBR ] =σ2ε∗∑n
i=1(Di − D)2(1− R2j )
with σε∗2 =
∑ni=1 ε
∗2
i
n − k − 1=
SSRε∗
n − k − 1
where R2j is R2 from regression of D on all Bj variables and a constant.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 172 / 176
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When Does Blocking Help?
Imagine a model for a complete and blocked randomized design:
Yi = α + τCRDi + εi (1)
Yi = α + τBRDi +J∑
j=2
βjBij + ε∗i (2)
where Bj is a dummy for the j-th block. Then given iid sampling:
Var [τCR ] =σ2ε∑n
i=1(Di − D)2with σ2
ε =
∑ni=1 ε
2i
n − 2=
SSRεn − 2
Var [τBR ] =σ2ε∗∑n
i=1(Di − D)2(1− R2j )
with σε∗2 =
∑ni=1 ε
∗2
i
n − k − 1=
SSRε∗
n − k − 1
where R2j is R2 from regression of D on all Bj variables and a constant.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 172 / 176
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When Does Blocking Help?
Imagine a model for a complete and blocked randomized design:
Yi = α + τCRDi + εi (1)
Yi = α + τBRDi +J∑
j=2
βjBij + ε∗i (2)
where Bj is a dummy for the j-th block. Then given iid sampling:
Var [τCR ] =σ2ε∑n
i=1(Di − D)2with σ2
ε =
∑ni=1 ε
2i
n − 2=
SSRεn − 2
Var [τBR ] =σ2ε∗∑n
i=1(Di − D)2(1− R2j )
with σε∗2 =
∑ni=1 ε
∗2
i
n − k − 1=
SSRε∗
n − k − 1
where R2j
is R2 from regression of D on all Bj variables and a constant.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 172 / 176
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When Does Blocking Help?
Imagine a model for a complete and blocked randomized design:
Yi = α + τCRDi + εi (1)
Yi = α + τBRDi +J∑
j=2
βjBij + ε∗i (2)
where Bj is a dummy for the j-th block. Then given iid sampling:
Var [τCR ] =σ2ε∑n
i=1(Di − D)2with σ2
ε =
∑ni=1 ε
2i
n − 2=
SSRεn − 2
Var [τBR ] =σ2ε∗∑n
i=1(Di − D)2(1− R2j )
with σε∗2 =
∑ni=1 ε
∗2
i
n − k − 1=
SSRε∗
n − k − 1
where R2j is R2 from regression of D on all Bj variables and a constant.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 172 / 176
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When Does Blocking Help?
Yi = α + τCRDi + εi (3)
Yi = α + τBRDi +J∑
j=2
βjBij + ε∗i (4)
where Bk is a dummy for the k-th block. Then given iid sampling:
V [τCR ] =σ2ε∑n
i=1(Di − D)2with σ2
ε =
∑ni=1 ε
2i
n − 2=
SSRεn − 2
V [τBR ] =σ2ε∗∑n
i=1(Di − D)2(1− R2j )
with σε∗2 =
∑ni=1 ε
∗2
i
n − k − 1=
SSRε∗
n − k − 1
where R2j is R2 from regression of D on the Bk dummies and a constant.
So when is Var [τBR ] < Var [τCR ]?
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 173 / 176
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When Does Blocking Help?
Yi = α + τCRDi + εi (5)
Yi = α + τBRDi +J∑
j=2
βjBij + ε∗i (6)
where Bk is a dummy for the k-th block. Then given iid sampling:
V [τCR ] =σ2ε∑n
i=1(Di − D)2with σ2
ε =
∑ni=1 ε
2i
n − 2=
SSRεn − 2
V [τBR ] =σ2ε∗∑n
i=1(Di − D)2(1− R2j )
with σε∗2 =
∑ni=1 ε
∗2
i
n − k − 1=
SSRε∗
n − k − 1
where R2j is R2 from regression of D on the Bk dummies and a constant.
Since R2j ≈ 0 V [τBR ] < V [τCR ] if
SSRε∗
n−k−1 <SSRε
n−2
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 174 / 176
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Blocking
How does blocking help?
I Increases efficiency if the blocking variables predict outcomes (i.e. they“remove” the variation that is driven by nuisance factors)
I Blocking on irrelevant predictors can burn up degrees of freedom.I Can help with small sample bias due to “bad” randomizationI Is powerful especially in small to medium sized samples.
What to block on?
I “Block what you can, randomize what you can’t”I The baseline of the outcome variable and other main predictors.I Variables desired for subgroup analysis
How to block?
I StratificationI Pair-matchingI Check: blockTools library.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 175 / 176
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Blocking
How does blocking help?
I Increases efficiency if the blocking variables predict outcomes (i.e. they“remove” the variation that is driven by nuisance factors)
I Blocking on irrelevant predictors can burn up degrees of freedom.I Can help with small sample bias due to “bad” randomizationI Is powerful especially in small to medium sized samples.
What to block on?
I “Block what you can, randomize what you can’t”I The baseline of the outcome variable and other main predictors.I Variables desired for subgroup analysis
How to block?
I StratificationI Pair-matchingI Check: blockTools library.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 175 / 176
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Analysis with Blocking
“As ye randomize, so shall ye analyze” (Senn 2004): Need toaccount for the method of randomization when performing statisticalanalysis.
If using OLS, strata dummies should be included when analyzingresults of stratified randomization.
I If probability of treatment assignment varies across blocks, then weighttreated units by probability of being in treatment and controls by theprobability of being a control.
Failure to control for the method of randomization can result inincorrect test size.
Stewart (Princeton) Week 10: Measured Confounding November 28 and 30, 2016 176 / 176