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"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
"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 the 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 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
"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 2 1/2 centuries ago." Patricia Cheng Department of Psychology University of California, Los Angeles
"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
"This lucidly written book is full of inspiration and novel ideas that brings clarity to areas where confusion has prevailed, in particular concerning causal interpretation of structural equation systems, but also on concepts such as counterfactual reasoning and the general relation between causal thinking and graphical models. Finally the world can get a coherent exposition of these ideas that Judea Pearl has developed over a number of years and presented in a flurry of controversial yet illuminating articles." Steffen L. Lauritzen Department of Mathematics. Aalborg University, Denmark
"Judea Pearl's new book, Causality: Models, Reasoning and Inference, is an outstanding contribution to the causality literature. It will be especially useful to students and practitioners of economics interested in policy analysis." Halbert White Professor of Economics University of California, San Diego
"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 School of Public Health, University of California, Los Angeles
"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
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