structural analysis of confoundingħ THE LOGIC OF COUNTERFACTUALS AND STRUCTURAL MODELS
CAUSALITY CAUSALITY by Judea Pearl TABLE OF CONTENTS (updated 9/99) PREFACE (updated 9/99) 1 INTRODUCTION TO PROBABILITIES, GRAPHS, AND CAUSAL MODELS (updated 1/2000)ġ.1.2 Basic concepts in probability theoryġ.1.3 Combining predictive and diagnostic supportsġ.1.5 Conditional independence and graphoidsġ.3.1 Causal networks as oracles for interventionsġ.3.2 Causal relationships and their stabilityġ.4.2 Probabilistic predictions in causal modelsġ.4.3 Interventions and causal effects in functional modelsġ.4.4 Counterfactuals in functional modelsĢ A THEORY OF INFERRED CAUSATION (updated 1/2000)Ģ.8 Non-Temporal Causation and Statistical TimeĢ.9 Conclusions 2.9.1 On minimality, Markov, and stability 3 CAUSAL DIAGRAMS AND THE IDENTIFICATION OF CAUSALģ.2.3 Computing the effect of interventionsģ.3.3 Example: Smoking and the genotype theoryģ.4.3 Symbolic derivation of causal effects: An exampleģ.4.4 Causal inference by surrogate experimentsģ.6.2 Diagrams as a mathematical languageģ.6.3 Translation from Graphs to Potential OutcomesĤ ACTIONS, PLANS, AND DIRECT EFFECTS (updated 2/2000)Ĥ.2 Conditional Actions and Stochastic PoliciesĤ.3 When is the Effect of an Action Identifiable?Ĥ.3.1 Graphical conditions for identificationĤ.3.3 Deriving a closed-form expression for control queriesĤ.4.2 Plan identification: Notation and assumptionsĤ.4.3 Plan identification: A general criterionĤ.5 Direct Effects and their IdentificationĤ.5.2 Direct effects, definition and identificationĤ.5.3 Example: Sex Discrimination in College Admissionĥ CAUSALITY AND STRUCTURAL MODELS IN THE SOCIAL SCIENCES (updated 3/2000)ĥ.1.3 Graphs as a mathematical language: An exampleĥ.2.1 The testable implications of structural modelsĥ.3.1 Parameter identification in linear modelsĥ.3.2 Comparison to nonparametric identificationĥ.3.3 Causal effects: The interventional interpretation of structural equation modelsĥ.4.1 What do structural parameters really mean?ĥ.4.2 Interpretation of effect decompositionĥ.4.3 Exogeneity, superexogeneity and other frillsĦ SIMPSON'S PARADOX, CONFOUNDING, AND COLLAPSIBILITY (updated 2/2000 (final corrections inserted but not proofed))Ħ.1.4 A paradox resolved (or what kind of machine is man)Ħ.2 Why there is no statistical test for confounding, why many think there is, and why they are almost rightĦ.2.2 Causal and associational definitionsĦ.3.1 Failing sufficiency due to marginalityĦ.3.2 Failing sufficiency due to closed-world assumptionsĦ.3.3 Failing necessity due to barren proxiesĦ.3.4 Failing necessity due to incidental cancelationsĦ.4.3 Operational test for stable no-confoundingĦ.5 Confounding, Collapsibility, and ExchangeabilityĦ.5.3 Exchangeability vs.
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