Causality: Models, Reasoning, and Inference 1st Edition
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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."
"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
- Item Weight : 1.97 pounds
- Hardcover : 400 pages
- ISBN-10 : 0521773628
- ISBN-13 : 978-0521773621
- Dimensions : 7.36 x 1.1 x 10.39 inches
- Publisher : Cambridge University Press; 1st Edition (March 13, 2000)
- Language: : English
- Best Sellers Rank: #1,554,883 in Books (See Top 100 in Books)
- Customer Reviews:
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Moreover, I do not think that this book presents state of the art information about our current knowledge of this subject. For example, the important problem to extract a network structure (structure learning) from data rather than estimating the parameters of a given networks structure is completely missing.
Nevertheless, this is a good book, because it might give you in the long run (you can not read it in one piece) insights you did not have before. Of course not to all topics causality is involved (see, e.g., above) but the given topics are thorough explained albeit on an advanced level.
Update: I add one star (total three) to my evaluation, because in the meanwhile I appreciate the historical development described in the book including references to the literature.
But the style is sufficiently dense and dry we will need some additional books with more practical styles before these ideas become widely understood. The style is fairly good by the standards of books whose main goal is rigorous proof, but it's still hard work to learn a large number of new concepts that are mostly referred to by terse symbols whose meaning can't be found via a glossary or index. Pearl occasionally introduces a memorable word, such as do(x), the way a software engineer who wants readable code would, but mostly sticks to single-character symbols that seem unreasonably hard (at least for us programmers who are used to descriptive names) to remember.
If you're uncertain whether reading this book is worth the effort, I strongly recommend reading the afterword first. It ought to have been used as the introduction, and without it many readers will be left wondering why they should believe they will be rewarded for slogging through so much dry material.
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.