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Title: Causality : Models, Reasoning, and Inference by Judea Pearl ISBN: 0-521-77362-8 Publisher: Cambridge University Press Pub. Date: 13 March, 2000 Format: Hardcover Volumes: 1 List Price(USD): $43.00 |
Average Customer Rating: 3.89 (9 reviews)
Rating: 5
Summary: A Pioneering Book on Causality
Comment: This is a pioneering book dealing exhaustively with the subject of causation. Its contribution to the field of "Uncertainty in AI" is unmeasureable. It dealt with graphical models for reasoning in depth. For computer scientists looking for an encyclopedia of algorithms and applications on causation, there can not be a better book. I highly recommend this book for researchers in UAI. A word of caution: This is not a book for starters and those who do not have a well developed concept of uncertainty.
Rating: 3
Summary: A review of "Causality"
Comment: First off, the rating of three stars is relative to my expectations that this book would provide me with some insights in how to use graphical models for purposes of making inferences from statistical data and, in general, to facilitate the process of (machine) learning from data. And although Pearl and his colleagues have made great progress in this area, this book seems more targeted for researchers in areas outside of AI, such as economics, statistics, and medical research. Although the author gives a number of rigorous definitions to help support his notions of causality, the book is written in a somewhat abstract manner with few if any nontrivial examples (although enough trivial ones to satisfy a more general audience) to support the definitions and concepts. References to the literature are favored over mathematical proofs. Thus, aside from the references, I found this book of little use, but on the other hand, I do recommend it for its intended audience, for I do believe that graphical models can be of great use in these other areas.
Finally given the controversy and general misunderstanding about "causality", I wonder why Pearl would even use definitions like "causal model" and "...variable X is a causal influence of variable Y". His justification seems that researchers still think in terms of cause and effect, and thus it would serve them well if they had a mathematical foundation to fall back on.
Even if I did not have issue with some of the techniques and algorithms endorsed in this book, it would still seem much more appropriate to supply fresh, distinguished definitions (devoid of the "cause" word and its synonyms) and thus when future researchers use and make reference to Pearl's structural methods, they will call them as such and hopefully avoid confusion and controversy.
Rating: 5
Summary: A "Radically New perspective on Causation"
Comment: Choice (November 00) calls both Pearl's Causality (and Juarrero's Dynamics in Action, which Choice reviews together with Pearl), a "radically new perspective on causation and human behavior... Pearl critically reviews the major literature on causation, both in philosohy and in applied statistics in the social sciences. His formal models, nicely illustrated by practical examples, show the power of positing objectdively real causation connetions, counter to Hume's skepticism, which has dominated discussions of causality in both analytic philosophy and statistical analysis. Probabilities, Pearl argues, reflect subjective degrees of belief, whereas causal relations describe objective physical constraints. He reveals the role of substantive causes in statistical analyses in examples from medicine, economics, and policy decisions. "Both works are highly ambitious in rejecting traditional views. Although the arguments ar meticulous and represent intensive research, their criticisms of mainstream traditions are destined to arouse controversy... Juarrero and Pearl's books will greatly interest philosophers and scientists who are concerned with causality and the explanation of human behavior."
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Title: Probabilistic Reasoning in Intelligent Systems : Networks of Plausible Inference by Judea Pearl ISBN: 1558604790 Publisher: Morgan Kaufmann Pub. Date: 01 September, 1988 List Price(USD): $73.95 |
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Title: Learning Bayesian Networks by Richard E. Neapolitan ISBN: 0130125342 Publisher: Prentice Hall Pub. Date: 01 April, 2003 List Price(USD): $74.00 |
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Title: Bayesian Networks and Decision Graphs by Finn V. Jensen ISBN: 0387952594 Publisher: Springer Verlag Pub. Date: 06 July, 2001 List Price(USD): $74.95 |
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Title: Causality and Explanation by Wesley C. Salmon ISBN: 0195108647 Publisher: Oxford University Press Pub. Date: January, 1998 List Price(USD): $29.95 |
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Title: Learning in Graphical Models (Adaptive Computation and Machine Learning) by Michael I. Jordan ISBN: 0262600323 Publisher: MIT Press Pub. Date: 27 November, 1998 List Price(USD): $70.00 |
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