Causal inference in practice: Here, there, causality is everywhere
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Here, there, causality is everywhere Amit Sharma Microsoft Research, New York
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Transcript of Causal inference in practice: Here, there, causality is everywhere
Here, there, causality is everywhereAmit SharmaMicrosoft Research, New York
My route to causality
Building recommender
systems in social Networks
Conducting user
experiments
Estimating impact of
recommendations and social
feeds
Causality is everywhere Spans every branch of science. Aristotle: to know, is to know the final cause.
Two approaches: Randomized experiments (Fisher): Gold standard Observational data: Messy.
Outline Causality is everywhere Economics Political Science Human Behavior Biology and Medicine Online systems Estimating causality using graphical models Conditioning Mechanism-based Natural Experiments The promise of graphical models
Causality in economics
David Card. The causal effect of education on earnings (1999)
Conley and Heerwig. The Long-Term Effects of Military Conscription on Mortality: Estimates From the Vietnam-Era Draft Lottery (2012)
Causality in political science
Darrell West. Air Wars (2013)Chattopadhyay and Duflo. Women as Policy Makers: Evidence from a Randomized Policy Experiment inIndia (2004)
Causality in human behavior
Thistlewaithe and Campbell. Effect of public recognition of scholastic achievement (1960)
Christakis and Fowler. The collective dynamics of smoking in a large social network (2008)
Causality in biology and medicine
Effect of Vitamin D deficiency on colon cancer
Effect of heart attack surgery on long-term health of patient
Causality in web applications
Sharma and Cosley. Distinguishing between personal preference and homophily in online activity feeds (2016).
Sharma, Hofman and Watts. Estimating the causal impact of recommender systems (2015).
Methods for estimating causal effects from observational data
Condition on observed covariates
• Stratification• Matching• Regression (?)
Mechanism-based strategies
• Path-based approaches
Natural experiments
• As-if experiments
• Instrumental Variables
• Regression discontinuity
Towards unifying estimation strategies: Causal graphical models
I. Ideal: Randomized experiments
II. Conditioning on observed covariatesCorresponds to Backdoor criterion.
a) StratificationCondition on different levels of socio-economic status.
b) Matching Socio-Economic status is a function of parents’ income, locality and other observed indicators.
b) Matching Model propensity to attend a particular school.
Pschool = f(PI, Loc, …)
c) RegressionCondition on observed covariates by adding them as independent variables in regression.
Works only if true causalrelationship between variables is linear.
III. Mechanism-based strategies Corresponds to Front door criterion.
IV. Natural Experiments Look for experiments happening in the real world.
Promise greater generalizability than controlled lab experiments.
Require greater care to ensure validity of causal identification.
a. (As-if) random experiments
b) Instrumental variables
Shock! Increase in traffic
c) Regression discontinuity
The promise of graphical models
Which variables to condition on?
Two graphical criteria explain all of conventional approaches A principled, succinct framework for causality.
Allows arbitrary functional forms for relationships between variables.
Leads to clear statements about causal assumptions.
If a causal effect can be identified, it can be derived using do-calculus (helpful for bigger graphs).
Graphical models form a succinct, consistent and complete framework for causality.They are also practical.
thank you!Amit Sharmahttp://www.amitsharma.in
