Causality: Models, Reasoning, and Inference (Hardcover)

~ (Author) "Causality connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity..." (more)
Key Phrases: structural model semantics, causal beam, covariate selection problem, James Robins, Karl Pearson, Jin Tian (more...)

Editorial Reviews

Review

"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

"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

"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 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

"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

"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 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

"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

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."
Journal of statistical Computation and Simulation

"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

See all Editorial Reviews

Product Details

• Hardcover: 384 pages
• Publisher: Cambridge University Press (March 13, 2000)
• Language: English
• ISBN-10: 0521773628
• ISBN-13: 978-0521773621
• Product Dimensions: 10.2 x 7.3 x 1.2 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 3.8 out of 5 stars  See all reviews (12 customer reviews)
• Amazon.com Sales Rank: #350,053 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

56 of 61 people found the following review helpful:

5.0 out of 5 stars Pearl summarizes his work on causation., July 11, 2000

By	Mikel Aickin "mikelaickin" (Portland, OR USA) - See all my reviews

Judea Pearl and his colleagues at UCLA (and elsewhere) have published a large number of papers and written unpublished reports over the past 15 years, in which they have developed a modern, analytical approach to causation. Many of these are in somewhat obscure publications, and so it is especially helpful to have the most important of them collected together in this volume. Pearl has edited, written new chapters and connecting prose, to weave this summary of a substantial amount of research.

Although the dust-jacket suggests that only modest mathematics is needed, and although this is technically true, it is misleading, because the whole area requires a sophistication of thought that goes well beyond the simplicity of the tools. Nonetheless, there is currently no other volume that is as easy to read as this, and summarizes so much material so compactly.

It is possible that the new vision of causal analysis developed by Spirtes, Scheines, Glymour, Pearl, Robins, Verma, Heckerman, Meek, and others, will have profound effect on how we analyze research data. If so, this book will be necessary reading for decades to come.

32 of 34 people found the following review helpful:

5.0 out of 5 stars Pearl's view on causality, February 22, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

Judea Pearl is one of the leading researchers in the topic of casuality. What is causality? In the exploration of statistical data we are often able to find relationships or correlations between two variables. We are often tempted to attribute the results of one variable, say A as an outcome (being high or low)that is due to the result (high or low) of the other, say B. We want to say that B is the cause of the outcome of A. Significant correlation by itself only suggests relationships. It cannot tell you whether A causes B or B causes A or neither. Causility is the study of designing experiments to allow you to determine if a relationship has a cause and effect. The subject matter is very philosophical and somewhat controversial. But a lot of research effort has gone into providing mathematical rigor to the concept. Pearl is one of those rare scientists who can contribute to such theory and explain it. But as Aickin suggests in his amazon review this is not a subject for a novice. Previous exposure to statistical methods such as correlation and regression is important to a clear unbderstanding of this book.

24 of 25 people found the following review helpful:

5.0 out of 5 stars The best and only on the topic, May 19, 2001

By A Customer

A great text, if for no other reason than the fact that it fills an important niche. Pearl does an excellent job of delineating causal models as both philosophical and statistical problems. I found the coverage of latent variable models particularly useful.

My only complaint is Pearl often makes assumptions without justifying them sufficiently. Usually, the assumptions made are reasonable or of negligible consequence, but at other times, the veracity of the assumptions is arguably core matter of the discussion. The net effect is a feeling of reading a brilliant, detailed exposition of what causal models imply observationally, undermined by doubts about the appropriateness of causality as a concept at all.

Overall, however, this a wonderful text that should be useful to anyone interested in causality or statistical modeling.