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1
Searching for Causal Models
Richard Scheines
Philosophy, Machine Learning,
Human-Computer Interaction
Carnegie Mellon University
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Goals
1. Basic Familiarity with Causal Model Search:
o What it is
o What it can and cannot do
2. Basic Familiarity with Tetrad IV
o What it is
o What it can and cannot do
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Outline
1. Motivation
2. Representing Causal Systems
3. Strategies for Causal Inference
4. Causal Model Search
5. Examples
6. Causal Model Search with Latent Variables
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1. Motivation
• Conditioning ≠ Intervening : P(Y | X = x ) ≠ P(Y | X set= x)
• When and how can we use non-experimental data to tell us
about the effect of a future intervention?
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Motivation
Rumsfeld Problem: Do we know what we don’t know:
Can we tell when there is not enough information
in the data + background knowledge to
infer causation?
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Motivation: Example
Online Course:
• As good or better than lecture?
• What student behaviors cause learning?
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Full Semester Online Course in Causal & Statistical Reasoning
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Full Semester Online Course in Causal & Statistical Reasoning
Course is tooled to record certain events: Logins, page requests, print requests, quiz attempts, quiz
scores, voluntary exercises attempted, etc.
Each event was associated with attributes: Time student-id Session-id
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Experiments
• 2000 : Online vs. Lecture, UCSD
• Winter (N = 180)
• Spring (N = 120)
• 2001: Online vs. Lecture, Pitt & UCSD
• UCSD - winter (N = 190)
• Pitt (N = 80)
• UCSD - spring (N = 110)
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Online vs. Lecture Delivery
• Online:• No lecture / one recitation per week• Required to finish approximately 2 online modules / week
• Lecture:• 2 Lectures / one recitation per week• Printed out modules as reading – extra assignments
• Same Material, same Exams:• 2 Paper and Pencil Midterms• 1 Paper and Pencil Final Exam
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Pitt 2001 - Variables
Pre-test (%)
Midterm1 (%)
Midterm 2 (%)
Final Exam (%)
Recitation attendance (%)
Lecture attendance (%)
Gender
Online (1 = online, 0 = lecture)
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Online vs. Lecture - Pitt
• Online students did 1/2 a St.Dev better than lecture students (p = .059)
• Factors affecting performance: Practice Questions Attempted
• Cost: Online condition costs 1/3 less per student
Final Exam (%)
Recitation Attendance (%)
Online
.22
5.3
.23
-10
Pre-test (%)
df = 2c2 = 0.08p-value = .96
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Printing and Voluntary Comprehension Checks: 2002 --> 2003
.302
-.41
.75
.353
.323
pre
print voluntary questions
quiz
final
2002
-.08
-.16
.41
.25
pre
print voluntary questions
final
2003
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2. Representing Causal Systems
1. Causal structure - qualitatively
2. Interventions
3. Statistical Causal Models
1. Causal Bayes Networks
2. Structural Equation Models
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Causal Graphs
Causal Graph G = {V,E}
Each edge X Y represents a direct causal claim:
X is a direct cause of Y relative to V
Exposure Rash
Exposure Infection Rash
Chicken Pox
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Causal Graphs
Do Not need to be
Cause Complete
Do need to be Common Cause Complete
Exposure Infection Symptoms
Exposure Infection Symptoms
Omitted
Common Causes
Omitted Causes 2Omitted Causes 1
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Sweaters On
Room Temperature
Pre-experimental SystemPost
Modeling Ideal Interventions
Interventions on the Effect
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Modeling Ideal Interventions
SweatersOn
Room Temperature
Pre-experimental SystemPost
Interventions on the Cause
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Interventions & Causal GraphsModel an ideal intervention by adding an “intervention” variable
outside the original system as a direct cause of its target.
Education Income Taxes Pre-intervention graph
Intervene on Income
“Soft” Intervention
Education Income Taxes
I
“Hard” Intervention
Education Income Taxes
I
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Causal Bayes Networks
P(S = 0) = .7
P(S = 1) = .3
P(YF = 0 | S = 0) = .99 P(LC = 0 | S = 0) = .95
P(YF = 1 | S = 0) = .01 P(LC = 1 | S = 0) = .05
P(YF = 0 | S = 1) = .20 P(LC = 0 | S = 1) = .80
P(YF = 1 | S = 1) = .80 P(LC = 1 | S = 1) = .20
Smoking [0,1]
Lung Cancer[0,1]
Yellow Fingers[0,1]
P(S,YF, L) = P(S) P(YF | S) P(LC | S)
The Joint Distribution Factors
According to the Causal Graph,
i.e., for all X in V
P(V) = P(X|Immediate Causes of(X))
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Tetrad Demo
http://www.phil.cmu.edu/projects/tetrad_download/
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Structural Equation Models
1. Structural Equations
2. Statistical Constraints
Education
LongevityIncome
Statistical Model
Causal Graph
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Structural Equation Models
Structural Equations: One Equation for each variable V in the graph:
V = f(parents(V), errorV)for SEM (linear regression) f is a linear function
Statistical Constraints: Joint Distribution over the Error terms
Education
LongevityIncome
Causal Graph
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Structural Equation Models
Equations:
Education = ed
Income =Educationincome
Longevity =EducationLongevity
Statistical Constraints:
(ed, Income,Income ) ~N(0,2)
2diagonal
- no variance is zero
Education
LongevityIncome
Causal Graph
Education
Income Longevity
1 2
LongevityIncome
SEM Graph
(path diagram)
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Calculating the Effect of Interventions
Pre-manipulation Joint Distribution (YF,S,L)
Intervention, Causal Graph
Post-manipulation Joint Distribution (YF,S,L)
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Calculating the Effect of Interventions
Smoking [0,1]
Lung Cancer[0,1]
Yellow Fingers[0,1]
P(YF,S,L) = P(S) P(YF|S) P(L|S)
P(YF,S,L)m = P(S) P(YF|Manip) P(L|S)
Smoking [0,1]
Lung Cancer[0,1]
Yellow Fingers[0,1]
Manipulation
Replace pre-manipulation causes
with manipulation
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Structural Equations:
Education = ed
Longevity =f1(Education)Longevity
Income = f2(Education)income
Education
LongevityIncome
Modularity of Intervention/Manipulation
Causal
Graph
Manipulated Structural Equations:
Education = ed
Longevity =f1(Education)Longevity
Income = f3(M1)
Manipulated
Causal
Graph
Education
Longevity Income
M1
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Structural Equations:
Education = ed
Longevity =f1(Education)Longevity
Income = f2(Education)income
Education
LongevityIncome
Modularity of Intervention/Manipulation
Causal
Graph
Manipulated Structural Equations:
Education = ed
Longevity =f1(Education)Longevity
Income = f3(M2,Education) income
Manipulated
Causal
Graph
Education
Longevity Income
M2
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3. Strategies for Causal Inference
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Goal: Causation (X Y) Problem: Association Causation Why? -- Mainly confounding Solutions (Designs)
o Experiments Controlled Trials Randomized Trials
o Observational Studies Quasi-Experiments - Fortuitous Randomization Instrumental Variables Statistical Control
Quasi-Experiments – Blocking Interrupted Time Series
o Causal Model Search
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Statistical Evidence - Question 1: Is there an Association?
rTV,Obsesity ≠ 0
rTV,Obsesity = 0
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Statistical Evidence – Question 2: Is the Association Spurious?
rTV,Obsesity ≠ 0
Permissiveness of Parents
TV Obesity
Produced by:
TV Obesity
TV Obesity
Spurious Association
Causal Association
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The Problem of Confounding
TV Obesity
Permissiveness of Parents
C1 C2 Cn
??Contract $ # IEDs
Ethnic Alignment with Central Govt.
C1 C2 Cn
??
Hours of TV
BMI
Contract $
# IEDs
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Randomized Trials eliminate Spurious Association
Exposure (treatment) assigned randomly
In an RT: association between exposure and outcome: strong evidence of causation:
TV Obesity Randomizer
TV Obesity Randomizer
Permissiveness
TV Obesity
Randomizer
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Designs for Dealing With Confounding
Contract $
# IEDs
Ethnic Alignment
C1 C2 Cn
??Randomizer
1) Experiments - Randomized Trials
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Designs for Dealing With Confounding
Contract $
# IEDs
Ethnic Alignment
C1 C2 Cn
??Randomizer
1) Experiments - Randomized Trials
All confounders removed
Often Ethically or Practically Impossible
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Designs for Dealing With Confounding
Contract $ # IEDs
Ethnic Alignment
C1 C2 Cn
??
2a) Observational Studies - Statistical Control
rContract$,#IEDs
All confounders must be measured
.EthnicAlignment, C1, C2,..,Cn
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Eliminating Spurious Association without Randomizing/Assigning/Controlling Exposure
All confounders measured?
Permissiveness
of Parents
TV Obesity
Physical Activity
rTV,Obestity.Permissiveness ≠ 0
Confounders measured well?
Permissiveness
of Parents
TV Obesity
Poor Measure of
Permissiveness
rTV,Obestity.PoorMeasure ≠ 0
Statistical Adjustment
(controlling for covariates)
Permissiveness of Parents
TV Obesity
rTV,Obestity.Permissiveness = 0rTV,Obestity.≠ 0
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Designs for Dealing With Confounding
2b) Observational Studies - Instrumental Variables
Contracting Agent(Z)
Needed Assumptions:• Z direct cause of Contract $• Z independent of every confounder
Contract $ # IEDs
Ethnic Alignment with Central Govt.
C1 C2 Cn
??
Idea:• Z is a partial natural randomizer
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Designs for Dealing With Confounding
Gender-matchedInstructor Learning
C1 C2 Cn
??
2c) Observational Studies:Quasi-Experiments – Fortuitous Randomization
RandomAssignment of
Instructor
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Designs for Dealing With Confounding
Gender-matchedInstructor Learning
C1 C2 Cn
??
2c) Observational Studies:Quasi-Experiments – Fortuitous Randomization
RandomAssignment of
Instructor
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Designs for Dealing With Confounding
TV Obesity
Permissiveness of Parents
C1 C2 Cn
??
2c) Quasi-Experiments - Blocking
Identical Twins
Subset Data to only Twins
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Strategies for Dealing With Confounding
TV Obesity
Permissiveness of Parents
C1 C2 Cn
??
2c) Quasi-Experiments - Blocking
Identical Twins
TV,Obesity in Twin 1 vs. TV,Obesity in Twin 2
Subset Data to only Twins
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Regression & Causal Inference
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Regression & Causal Inference
2. So, identifiy and measure potential confounders Z:
a) prior to X,
b) associated with X,
c) associated with Y
Typical (non-experimental) strategy:1. Establish a prima facie case (X associated with Y)
3. Statistically adjust for Z (multiple regression)
X Y
Z
But, omitted variable bias
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Regression & Causal Inference
Strategy threatened by measurement error – ignore this for now
Multiple regression is provably unreliable
for causal inference unless:• X prior to Y • X, Z, and Y are causally sufficient (no confounding)
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Examples
X
Y
Z
X
Y
Z2 Z1
T1
T2
X
Y
Z
T2
T1
Truth Regression Alternative?
bX = 0
bZ ≠ 0
bX ≠ 0
bZ ≠ 0
bX ≠ 0
bZ1 ≠ 0
bZ2 ≠ 0
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Better Methods Exist
Causal Model Search (since 1988):
• Provably Reliable
• Provably Rumsfeld
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4. Causal Model Search
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Causal Discovery
Statistical Data Causal Structure
Background Knowledge
- X2 before X3
- no unmeasured common causes
X3 | X2 X1
Independence Relations
Data
Statistical Inference
X2 X3 X1
Equivalence Class of Causal Graphs
X2 X3 X1
X2 X3 X1
Discovery Algorithm
Causal Markov Axiom (D-separation)
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Faithfulness
Constraints on a probability distribution P generated by a causal structure G hold for all parameterizations of G.
Revenues = aRate + cEconomy + eRev.
Economy = bRate + eEcon.
Faithfulness: a ≠ -bcTax Revenues
Economyc
ba
Tax Rate
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The Problem of Alternatives:Observationally Equivalent Models
Given an Experimental Setup, and Background Knowledge, and Theory, and a set of independence relations, what are all the models that would entail those independence relations that are consistent with BK and Theory?
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Equivalence Classes
• Independence (d-separation equivalence)• DAGs : Patterns• PAGs : Latent variable models• Intervention Equivalence Classes
• Measurement Model Equivalence Classes• Linear Non-Gaussian Model Equivalence Classes
Equivalence:• Independence (M1 ╞ X _||_ Y | Z M2 ╞ X _||_ Y | Z)
• Distribution (q1 q2 M1(q1) = M2(q2))
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Representations ofIndependence Equivalence Classes
We want the representations to:
• Characterize the Independence Relations Entailed by the Equivalence Class
• Represent causal features that are shared by every member of the equivalence class
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Patterns & PAGs
• Patterns (Verma and Pearl, 1990): graphical representation of Markov equivalence - with no latent variables.
• PAGs: (Richardson 1994) graphical representation of an equivalence class including latent variable models and sample selection bias that are Markov equivalent over a set of measured variables X
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Patterns
X2 X1
X2 X1
X2 X1
X4 X3
X2 X1
Possible Edges Example
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Patterns
X2
X4 X3
X1
X2
X4 X3
Represents
Pattern
X1 X2
X4 X3
X1
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PAGs: Partial Ancestral Graphs
X2
X3
X1
X2
X3
Represents
PAG
X1 X2
X3
X1
X2
X3
T1
X1
X2
X3
X1
etc.
T1
T1 T2
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Regression vs. PAGs
X
Y
Z
X
Y
Z2 Z1
T1
T2
X
Y
Z
T2
T1
X
Y
Z2 Z1
Truth Regression PAG
X
Y
Z1
X
Y
Z1bX = 0
bZ ≠ 0
bX ≠ 0
bZ ≠ 0
bX ≠ 0
bZ1 ≠ 0
bZ2 ≠ 0
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Causal Model Search
Background Knowledge
Data
Patterns
X2 X3 X1
PC, GES,
CPC
PAGs
X2 X3 X1
FCI, CFCI
DAGs
X2 X3 X1
Impossible
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Overview of Search Methods
Constraint Based Searches• TETRAD (SGS, PC, FCI)• Very fast – capable of handling 1,000 variables• Pointwise, but not uniformly consistent
Scoring Searches• Scores: BIC, AIC, etc.• Search: Hill Climb, Genetic Alg., Simulated Annealing• Difficult to extend to latent variable models• Meek and Chickering Greedy Equivalence Class (GES)• Very slow – max N ~ 30-40• Pointwise, but not uniformly consistent
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5. Examples
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Case Study 1: Foreign Investment
Does Foreign Investment in 3rd World Countries cause Political Repression?
Timberlake, M. and Williams, K. (1984). Dependence, political exclusion, and government repression: Some cross-national evidence. American Sociological Review 49, 141-146.
N = 72
PO degree of political exclusivity
CV lack of civil liberties
EN energy consumption per capita (economic development)
FI level of foreign investment
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Correlations
po fi en fi -.175 en -.480 0.330 cv 0.868 -.391 -.430
Case Study 1: Foreign Investment
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Regression Results
po = .227*fi - .176*en + .880*cv
SE (.058) (.059) (.060)
t 3.941 -2.99 14.6
Interpretation: foreign investment increases political repression
Case Study 1: Foreign Investment
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Alternatives
.217
FI
PO
CV En
Regression
.88 -.176
FI
PO
CV En
Tetrad - FCI
FI
PO
CV En
Fit: df=2, 2=0.12, p-value = .94
.31 -.23
.86 -.48
Case Study 1: Foreign Investment
There is no model with testable constraints (df > 0) in which FI has a positive effect on PO that is not rejected by the data.
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Variables
Tangibility/Concreteness (Exp manipulation)
Imaginability (likert 1-7)
Impact (avg. of 2 likerts)
Sympathy (likert)
Donation ($)
Case Study 2: Charitable Giving
Cryder & Loewenstein (in prep)
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Theoretical Model
Case Study 2: Charitable Giving
Imaginability Tangibility
Impact
Sympathy
Donation
study 1 (N= 94) df = 5, c2 = 52.0, p= 0.0000
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GES Outputs
Case Study 2: Charitable Giving
Imaginability Tangibility
Impact
Sympathy
Donation
study 1: df = 5, c2 = 5.88, p= 0.32
Imaginability Tangibility
Impact
Sympathy
Donation
study 1: df = 5, c2 = 3.99, p= 0.55
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Theoretical Model
Case Study 2: Charitable Giving
Imaginability Tangibility
Impact
Sympathy
Donation
study 2 (N= 115) df = 5, c2 = 62.6, p= 0.0000
Imaginability Tangibility
Impact
Sympathy
Donation
Imaginability Tangibility
Impact
Sympathy
Donation
study 2: df = 5, c2 = 8.23, p= 0.14
study 2: df = 5, c2 = 7.48, p= 0.18
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GES Outputs
Case Study 2: Charitable Giving
Imaginability Tangibility
Impact
Sympathy
Donation
study 1: df = 5, c2 = 5.88, p= 0.32
Imaginability Tangibility
Impact
Sympathy
Donation
study 2: df = 5, c2 = 8.23, p= 0.14
study 1: df = 5, c2 = 3.99, p= 0.55
study 2: df = 5, c2 = 7.48, p= 0.18
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Lead and IQ: Variable Selection
BackwardsStepwise Regression
Measured Lead +5 Covariates
Measured Lead +39 Covariates
Final Variables (Needleman)
-lead baby teeth-fab father’s age-mab mother’s age-nlb number of live
births-med mother’s
education-piq parent’s IQ-ciq child’s IQ
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Needleman Regression
- standardized coefficient
- (t-ratios in parentheses)
- p-value for significance
ciq = - .143 lead - .204 fab - .159 nlb + .219 med + .237 mab + .247 piq
(2.32) (1.79) (2.30) (3.08) (1.97) (3.87)
0.02 0.09 0.02 <0.01 0.05 <0.01
All variables significant at .1 R2 = .271
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TETRAD Variable Selection
Tetradmab _||_ ciq
fab _||_ ciq
nlb _||_ ciq | med
ciq
mab fab nlb
lead piq med
Regressionmab _||_ ciq | { lead, med, piq, nlb fab} fab _||_ ciq | { lead, med, piq, nlb mab}
nlb _||_ ciq | { lead, med, piq, mab, fab}
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Regressions
- standardized coefficient
- (t-ratios in parentheses)
- p-value for significance
Needleman (R2 = .271)
ciq = - .143 lead - .204 fab - .159 nlb + .219 med + .237 mab + .247 piq
(2.32) (1.79) (2.30) (3.08) (1.97) (3.87)
0.02 0.09 0.02 <0.01 0.05 <0.01
TETRAD (R2 = .243)
ciq = - .177 lead + .251 med + .253 piq
(2.89) (3.50) (3.59)
<0.01 <0.01 <0.01
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Measurement Error Measured regressor variables are proxies that involve
measurement error Errors-in-all-variables model for Lead’s influence on IQ
- underidentified
Actual LeadExposure
EnvironmentalStimulation
ciq
lead 3
2
111
1
ciq
lead
med
med
piq
piq
Geneticfactors
Strategies:
• Sensitivity Analysis
• Bayesian Analysis
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Prior over Measurement Error
Proportion of Variance from Measurement Error
Measured Lead Mean = .2, SD = .1 Parent’s IQ Mean = .3, SD = .15 Mother’s Education Mean = .3, SD = .15
Prior Otherwise uninformative
Actual LeadExposure
EnvironmentalStimulation
ciq
lead 3
2
111
1
ciq
lead
med
med
piq
piq
Geneticfactors
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Posterior
Expected if Normal
0
50
100
150
200
250
0
50
100
150
200
250
Expected if Normal
Frequency
LEAD->ciq
Distribution of LEAD->ciq
Zero
Robust over similar priors
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Using Needleman’s CovariatesWith similar prior, the marginal posterior:
Expected if Normal
0
20
40
60
80
100
120
140
020
40
60
80
100
120
140
160Expected if Normal
Frequency
LEAD->ciq
Distribution of LEAD->ciq
Very Sensitive to Prior Over Regressors
TETRAD eliminated
Zero
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6. Causal Model Search with Latent Variables
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The Causal Theory Formation Problem for Latent Variable Models
Given observations on a number of variables, identify the latent variables that underlie these variables and the causal relations among these latent concepts.
Example: Spectral measurements of solar radiation intensities. Variables are intensities at each measured frequency.
Example: Quality of a Child’s Home Environment, Cumulative Exposure to Lead, Cognitive Functioning
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The Most Common Automatic Solution: Exploratory Factor Analysis
Chooses “factors” to account linearly for as much of the variance/covariance of the measured variables as possible.
Great for dimensionality reduction Factor rotations are arbitrary Gives no information about the statistical and thus the
causal dependencies among any real underlying factors.
No general theory of the reliability of the procedure
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Other Solutions
Independent Components, etcBackground TheoryScales
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Other Solutions: Background Theory
St1
12
Home
St2
12
St21
12
.
.
T1
Lead
.
.
Cognitive Function
T2
T20
C1 C2 C20 . .
?
Key Causal Question
Thus, key statistical question: Lead _||_ Cog | Home ?
Specified Model
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St1
12
Home
St2
12
St21
12
.
.
T1
Lead
.
.
Cognitive Function
T2
T20
C1 C2 C20 . .
F
Lead _||_ Cog | Home ?
Yes, but statistical inference will say otherwise.
Other Solutions: Background Theory
True Model
“Impurities”
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F1
x1 x2
F2 F3
x3 x4 y1 y2 y3 y4 z1 z2 z3 z4
Purify
Specified Model
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F1
x1 x2
F
F2 F3
x3 x4 y1 y2 y3 y4 z1 z2 z3 z4
Purify
True Model
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F1
x1 x2
F
F2 F3
x3 x4 y1 y2 y3 y4 z1 z2 z3 z4
Purify
True Model
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F1
x1 x2
F
F2 F3
x3 x4 y1 y2 y3 y4 z1 z2 z3 z4
Purify
True Model
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F1
x1 x2
F
F2 F3
x3 x4 y1 y2 y3 y4 z1 z2 z3 z4
Purify
True Model
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F1
x1 x2
F
F2 F3
x3 y1 y2 y3 y4 z1 z3 z4
Purify
Purified Model
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Scale = sum(measures of a latent)
Other Solutions: Scales
St1
12
Home
St2
12
St21
12
.
.
Homescale = i=1 to 21 (Sti)
Homescale
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True Model
Other Solutions: Scales
Pseudo-Random Sample: N = 2,000
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Scales vs. Latent variable Models
Regression:Cognition on Home, Lead
Predictor Coef SE Coef T PConstant -0.02291 0.02224 -1.03 0.303Home 1.22565 0.02895 42.33 0.000Lead -0.00575 0.02230 -0.26 0.797 S = 0.9940 R-Sq = 61.1% R-Sq(adj) = 61.0%
Insig.
True Model
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Scales vs. Latent variable Models
Scales
homescale = (x1 + x2 + x3)/3leadscale = (x4 + x5 + x6)/3cogscale = (x7 + x8 + x9)/3
True Model
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Scales vs. Latent variable Models
Cognition = - 0.0295 + 0.714 homescale - 0.178 Lead Predictor Coef SE Coef T PConstant -0.02945 0.02516 -1.17 0.242homescal 0.71399 0.02299 31.05 0.000Lead -0.17811 0.02386 -7.46 0.000
Regression:Cognition on homescale,
Lead
Sig.
True Model
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Scales vs. Latent variable Models
Modeling Latents
True Model
Specified Model
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Scales vs. Latent variable Models
(c2 = 29.6, df = 24, p = .19)
B5 = .0075, which at t=.23, is correctly insignificant
True Model
Estimated Model
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Scales vs. Latent variable Models
Mixing Latents and Scales
(c2 = 14.57, df = 12, p = .26)
B5 = -.137, which at t=5.2, is incorrectly highly significantP < .001
True Model
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Build Pure Clusters
Output - provably reliable (pointwise consistent):
Equivalence class of measurement models over a pure subset of measures
L1 L2 L3
m1 m2 m3 m4 m5 m6 m7 m8 m9
Stress Dep Health
m1 m2 m3 m4 m5 m6 m7 m8 m9 m11 m10
m
BPC
True Model
Output
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Build Pure ClustersQualitative Assumptions
1. Two types of nodes: measured (M) and latent (L)
2. M L (measured don’t cause latents)
3. Each m M measures (is a direct effect of) at least one l L
4. No cycles involving M
Quantitative Assumptions:
1. Each m M is a linear function of its parents plus noise
2. P(L) has second moments, positive variances, and no deterministic relations
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Case Study 4: Stress, Depression, and Religion
MSW Students (N = 127) 61 - item survey (Likert Scale)
• Stress: St1 - St21
• Depression: D1 - D20
• Religious Coping: C1 - C20
p = 0.00
St1
12
Stress
St2
12
St21
12
.
.
Dep1
12
Coping
.
.
Depression
Dep2
12
Dep20
12
C1 C2 C20 . .
+
- +
Specified Model
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Build Pure Clusters St3
12
Stress
St4
12 St16
12
Dep9
12
Coping
Depression Dep13
12 Dep19
12
C9 C12 C15
St18
12
St20
12
C14
Case Study 4: Stress, Depression, and Religion
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Assume Stress temporally prior:
MIMbuild to find Latent Structure: St3
12
Stress
St4
12 St16
12
Dep9
12
Coping
Depression Dep13
12 Dep19
12
C9 C12 C15
St18
12
St20
12
C14
+
+
p = 0.28
Case Study 4: Stress, Depression, and Religion
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Case Study 5: Test Anxiety
Bartholomew and Knott (1999), Latent variable models and factor analysis
12th Grade Males in British Columbia (N = 335)
20 - item survey (Likert Scale items): X1 - X20:
X2
Emotionality Worry
X8
X9
X10
X15
X16
X18
X3
X4
X5
X6
X7
X14
X17
X20
Exploratory Factor Analysis:
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Build Pure Clusters:
X2
Emotionalty
X8
X9
X10
X11
X16
X18
X3
X5
X7
X14
X6
Cares About Achieving
Self-Defeating
Case Study 5: Test Anxiety
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107
Build Pure Clusters:
X2
Emotionalty
X8
X9
X10
X11
X16
X18
X3
X5
X7
X14
X6
Worries About Achieving
Self-Defeating
X2
Emotionality Worry
X8
X9
X10
X15
X16
X18
X3
X4
X5
X6
X7
X14
X17
X20
p-value = 0.00 p-value = 0.47
Exploratory Factor Analysis:
Case Study 5: Test Anxiety
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X2
Emotionalty
X8
X9
X10
X11
X16
X18
X3
X5
X7
X14
X6
Worries About Achieving
Self-Defeating
MIMbuild
p = .43
Emotionalty-Scale
Worries About Achieving-Scale
Self-Defeating
Unininformative
Scales: No Independencies or Conditional
Independencies
Case Study 5: Test Anxiety
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Economics
Bessler, Pork Prices
Hoover, multiple
Other Cases
Educational Research
Easterday, Bias & Recall
Laski, Numerical coding
Climate Research
Glymour, Chu, , Teleconnections
Biology
Shipley,
SGS, Spartina Grass
Neuroscience
Glymour & Ramsey, fMRI
Epidemiology
Scheines, Lead & IQ
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Software
Education:
- Causality Lab: www.phil.cmu.edu/projects/causality-lab
- Web Course on Causal and Statistical Reasoning, and Empirical Research Methods: http://www.cmu.edu/oli/
Research:
Tetrad: www.phil.cmu.edu/projects/tetrad_download/
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References
Causation, Prediction, and Search, 2nd Edition, (2000), by P. Spirtes, C. Glymour, and R. Scheines ( MIT Press)
Causality: Models, Reasoning, and Inference (2000). By Judea Pearl, Cambridge Univ. Press
Computation, Causation, & Discovery (1999), edited by C. Glymour and G. Cooper, MIT Press
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References
Biology
Chu, Tianjaio, Glymour C., Scheines, R., & Spirtes, P, (2002). A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurement with Microarrays. Bioinformatics, 19: 1147-1152.
Shipley, B. Exploring hypothesis space: examples from organismal biology. Computation, Causation and Discovery. C. Glymour and G. Cooper. Cambridge, MA, MIT Press.
Shipley, B. (1995). Structured interspecific determinants of specific leaf area in 34 species of
herbaceous angeosperms. Functional Ecology 9.
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References
Scheines, R. (2000). Estimating Latent Causal Influences: TETRAD III Variable Selection and Bayesian Parameter Estimation: the effect of Lead on IQ, Handbook of Data Mining, Pat Hayes, editor, Oxford University Press.
Jackson, A., and Scheines, R., (2005). Single Mothers' Self-Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study, Social Work Research , 29, 1, pp. 7-20.
Timberlake, M. and Williams, K. (1984). Dependence, political exclusion, and government repression: Some cross-national evidence. American Sociological Review 49, 141-146.
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ReferencesEconomics
Akleman, Derya G., David A. Bessler, and Diana M. Burton. (1999). ‘Modeling corn exports and exchange rates with directed graphs and statistical loss functions’, in Clark Glymour and Gregory F. Cooper (eds) Computation, Causation, and Discovery, American Association for Artificial Intelligence, Menlo Park, CA and MIT Press, Cambridge, MA, pp. 497-520.
Awokuse, T. O. (2005) “Export-led Growth and the Japanese Economy: Evidence from VAR and Directed Acyclical Graphs,” Applied Economics Letters 12(14), 849-858.
Bessler, David A. and N. Loper. (2001) “Economic Development: Evidence from Directed Acyclical Graphs” Manchester School 69(4), 457-476.
Bessler, David A. and Seongpyo Lee. (2002). ‘Money and prices: U.S. data 1869-1914 (a study with directed graphs)’, Empirical Economics, Vol. 27, pp. 427-46.
Demiralp, Selva and Kevin D. Hoover. (2003) !Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics 65(supplement), pp. 745-767.
Haigh, M.S., N.K. Nomikos, and D.A. Bessler (2004) “Integration and Causality in International Freight Markets: Modeling with Error Correction and Directed Acyclical Graphs,” Southern Economic Journal 71(1), 145-162.
Sheffrin, Steven M. and Robert K. Triest. (1998). ‘A new approach to causality and economic growth’, unpublished typescript, University of California, Davis.
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ReferencesEconomics
Swanson, Norman R. and Clive W.J. Granger. (1997). ‘Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions’, Journal of the American Statistical Association, Vol. 92, pp. 357-67.
Demiralp, S., Hoover, K., & Perez, S. A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression Oxford Bulletin of Economics and Statistics, 2008, 70, (4), 509-533
- Searching for the Causal Structure of a Vector Autoregression Oxford Bulletin of Economics and Statistics, 2003, 65, (s1), 745-767
Kevin D. Hoover, Selva Demiralp, Stephen J. Perez, Empirical Identification of the Vector Autoregression: The Causes and Effects of U.S. M2*, This paper was written to present at the Conference in Honour of David F. Hendry at Oxford University, 2325 August 2007.
Selva Demiralp and Kevin D. Hoover , Searching for the Causal Structure of a Vector Autoregression, OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 65, SUPPLEMENT (2003) 0305-9049
A. Moneta, and P. Spirtes “Graphical Models for the Identification of Causal Structures in Multivariate Time Series Model”, Proceedings of the 2006 Joint Conference on Information Sciences, JCIS 2006, Kaohsiung, Taiwan, ROC, October 8-11,2006, Atlantis Press, 2006.
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References
Eberhardt, F., and Scheines R., (2007).“Interventions and Causal Inference”, in PSA-2006, Proceedings of the 20th biennial meeting of the Philosophy of Science Association 2006 http://philsci.org/news/PSA06
Silva, R., Glymour, C., Scheines, R. and Spirtes, P. (2006) “Learning the Structure of Latent Linear Structure Models,” Journal of Machine Learning Research, 7, 191-246.