Pearl is a member of the National Academy of Sciences, the National Academy of Engi­neering , and a Founding Fellow of the Association for Artificial Intelligence. He is a recipient of numerous prizes and awards, including the Technion's Harvey Prize and the ACM Alan Turing Award for fundamental contributions  to probabilistic and causal reasoning.

Madelyn Glymour is a data analyst at Carnegie Mellon University, and a science writer and editor for the Cognitive Systems Laboratory at UCLA. Her interests lie in causal discovery and in the art of making complex concepts accessible to broad audiences.

Nicholas P. Jewell is Professor of Biostatistics and Statistics at the University of California, Berkeley. He has held various academic and administrative positions at Berkeley since his arrival in 1981, most notably serving as Vice Provost from 1994 to 2000. He has also held academic appointments at the University of Edinburgh, Oxford University, the London School of Hygiene and Tropical Medicine, and at the University of Kyoto. In 2007, he was a Fellow at the Rockefeller Foundation Bellagio Study Center in Italy.

Jewell  is a Fellow  of the American  Statistical  Association,  the Institute of  Mathematical Statistics, and the American Association for the Advancement of Science (AAAS). He is a past winner of the Snedecor Award and the Marvin Zelen Leadership Award in Statistical Science from Harvard University. He is currently the Editor of the Journal of the American Statistical Association - Theory & Methods , and Chair of the Statistics Section of AAAS. His research focuses on the application of statistical methods to infectious and chronic disease epidemiology, the assessment of drug safety, time-to-event analyses, and human rights.

Videos: 

Introduction to Causality – Part I with Professors Judea Pearl and Nicholas P. Jewell

Teaching Causality – Part I with Professors Judea Pearl and Rob Gould

About the Authors ix

Preface xi

List of Figures xv

About the Companion Website xix

1 Preliminaries: Statistical and Causal Models 1

1.1 Why Study Causation 1

1.2 Simpson’s Paradox 1

1.3 Probability and Statistics 7

1.3.1 Variables 7

1.3.2 Events 8

1.3.3 Conditional Probability 8

1.3.4 Independence 10

1.3.5 Probability Distributions 11

1.3.6 The Law of Total Probability 11

1.3.7 Using Bayes’ Rule 13

1.3.8 Expected Values 16

1.3.9 Variance and Covariance 17

1.3.10 Regression 20

1.3.11 Multiple Regression 22

1.4 Graphs 24

1.5 Structural Causal Models 26

1.5.1 Modeling Causal Assumptions 26

1.5.2 Product Decomposition 29

2 Graphical Models and Their Applications 35

2.1 Connecting Models to Data 35

2.2 Chains and Forks 35

2.3 Colliders 40

2.4 d-separation 45

2.5 Model Testing and Causal Search 48

3 The Effects of Interventions 53

3.1 Interventions 53

3.2 The Adjustment Formula 55

3.2.1 To Adjust or not to Adjust? 58

3.2.2 Multiple Interventions and the Truncated Product Rule 60

3.3 The Backdoor Criterion 61

3.4 The Front-Door Criterion 66

3.5 Conditional Interventions and Covariate-Specific Effects 70

3.6 Inverse Probability Weighing 72

3.7 Mediation 75

3.8 Causal Inference in Linear Systems 78

3.8.1 Structural versus Regression Coefficients 80

3.8.2 The Causal Interpretation of Structural Coefficients 81

3.8.3 Identifying Structural Coefficients and Causal Effect 83

3.8.4 Mediation in Linear Systems 87

4 Counterfactuals and Their Applications 89

4.1 Counterfactuals 89

4.2 Defining and Computing Counterfactuals 91

4.2.1 The Structural Interpretation of Counterfactuals 91

4.2.2 The Fundamental Law of Counterfactuals 93

4.2.3 From Population Data to Individual Behavior – An Illustration 94

4.2.4 The Three Steps in Computing Counterfactuals 96

4.3 Nondeterministic Counterfactuals 98

4.3.1 Probabilities of Counterfactuals 98

4.3.2 The Graphical Representation of Counterfactuals 101

4.3.3 Counterfactuals in Experimental Settings 103

4.3.4 Counterfactuals in Linear Models 106

4.4 Practical Uses of Counterfactuals 107

4.4.1 Recruitment to a Program 107

4.4.2 Additive Interventions 109

4.4.3 Personal Decision Making 111

4.4.4 Sex Discrimination in Hiring 113

4.4.5 Mediation and Path-disabling Interventions 114

4.5 Mathematical Tool Kits for Attribution and Mediation 116

4.5.1 A Tool Kit for Attribution and Probabilities of Causation 116

4.5.2 A Tool Kit for Mediation 120

References 127

Index 133

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