Causality : Models, Reasoning and Inference

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers’ questions, and offers a panoramic view of recent advances in this field of research. Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics. – Table of Contents : 1. Introduction to probabilities, graphs, and causal models; – 2. A theory of inferred causation; – 3. Causal diagrams and the identification of causal effects; – 4. Actions, plans, and direct effects; – 5. Causality and structural models in the social sciences; – 6. Simpson's paradox, confounding, and collapsibility; – 7. Structural and counterfactual models; – 8. Imperfect experiments: bounds and counterfactuals; – 9. Probability of causation: interpretation and identification. – Epilogue: The art and science of cause and effect. M.-M. V.