Synopses & Reviews
\[ul\]\[li\]Presents the first unified account of the various approaches to causation\[li\]Offers simple mathematical tools for analyzing the relationships between causal connections and statistical associations\[li\]Provides students with natural models, simple identification procedures, and perecise mathematical definitions of causal concepts\[li\]Treats such concepts as: actions, causal-effect, exogeneity, confounding, direct and indirect effects, ignorability, Simpson's paradox, structural equations, path coefficients, instrumental variables, attribution, explanation, probability of causation, and single-event causation \[/ul\]\[p\]Written by one of the pre-eminent 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 a wide variety of fields. \[/p\]\[p\]The author presents and unifies the probabilistic, manipulative, counterfactual and structural approaches to causation, and he devises simple mathematical tools for studying the relationships between causal connections and statistical associations. \[i\]Causality\[/i\] will be of interest to professionals, students, and anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech. \[/p\] \[p\]Specific areas of application include: \[ul\]\[li\]artificial intelligence\[li\]statistics\[li\]business\[li\]cognitive science\[li\]computers\[li\]data mining\[li\]decision analysis\[li\]economics\[li\]engineering\[li\]epidemiology \[li\]health sciences\[li\]machine learning \[li\]mathematics \[li\]philosophy \[li\]prediction theory \[li\]psychology \[li\]social science \[li\]biostatistics \[/ul\]
Review
"...thought provoking and [a] valuable addition to the scientific community. The author, Judea Pearl, is not only an expert but also well known for creating novel ideas in cognitive system analysis and artificial intelligence...It is a well-composed an written book. The bibliography is exhaustive and up-to-date. I enjoyed thoroughly reading the material in the book. I would highly recommend this book to both theoretical and applied scientists." J. Statist. Comput. Simul."Without assuming much beyond elementary probability theory, Judea Pearl's book provides an attractive tour of recent work, in which he has played a central role, on causal models and causal reasoning. Due to his efforts, and that of a few others, a Renaissance in thinking and using causal concepts is taking place." Patrick Suppes, Center for the Study of Language and Information, Stanford University"For philosophers of science with a serious interest in casual modeling, Causality is simply mandatory reading." Philosophical Review"This highly original book will change the way social science researchers think about causality for years to come. Pearl has produced a new and powerful formal theory of causal analysis that will be great use to the serious empirical researcher. A must read." Christopher Winship, Department of Sociology, Harvard University"Judea Pearl's previous book, Probabilistic Reasoning in Intelligent Systems, was arguably the most influential book in Artificial Intelligence in the past decade, setting the stage for much of the current activity in probabilistic reasoning. In this book, Pearl turns his attention to causality, boldly arguing for the primacy of a notion long ignored in statistics and misunderstood and mistrusted in other disciplines, from physics to economics. He demystifies the notion, clarifies the basic concepts in terms of graphical models, and explains the source of many misunderstandings. This book should prove invaluable to researchers in artificial intelligence, statistics, economics, epidemiology, and philosophy, and, indeed, all those interested in the fundamental notion of causality. It may well prove to be one of the most influential books of the next decade." Joseph Halpern, Computer Science Department, Cornell University"Judea Pearl has come to statistics and causation with enthusiasm and creativity. His work is always thought provoking and worth careful study. This book proves to be no exception. Time and again I found myself disagreeing both with his assumptions and with his conclusions, but I was also fascinated by new insights into problems I thought I already understood well. This book illustrates the rich contributions Pearl has made to statistical literature and to our collective understanding of models for causal reasoning." Stephen Fienberg, Maurice Falk University Professor of Statistics and Social Science, Carnegie Mellon University"This book on causal inference by a brilliant computer scientist will both delight and inform all--philosophers, psychologists, epidemiologists, computer scientists, lawyers--who appreciate the intriguing problem of causation posed by David Hume more than two and a half centuries ago." Patricia Cheng, Department of Pyschology, University of California, Los Angeles"This book fulfills a long-standing need for a rigorous yet accessible treatise on the mathematics of causal inference. Judea Pearl has done a masterful job of describing the most important approaches and displaying their underlying logical unity. The book deserves to be read by all statisticians and scientists who use nonexperimental data to study causation, and would serve well as a graduate or advanced undergraduate course text." Sander Greenland, UCLA School of Public Health"Judea Pearl has written an account of recent advances in the modeling of probability and cause, substantial parts of which are due to him and his co-workers. This is essential reading for anyone interested in causality." Brian Skyrms, Department of Philosophy, University of California, Irvine"In conclusion, make no mistake about it: This is an important book. Even if almost all of the content has appeared previously in diverse venues, it has been brought together here for all of us to think about." Journal of American Statistical Association, Charles R. Hadlock, Bentley College
Synopsis
Causality offers the first comprehensive coverage of causal analysis in many sciences.
Synopsis
Written by one of the pre-eminent 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, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
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.