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Causality: Models, Reasoning, and Inference von Judea Pearl (2000, Hardcover)
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eBay-Artikelnr.:357366878672
Artikelmerkmale
- Artikelzustand
- ISBN
- 9780521773621
Über dieses Produkt
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
0521773628
ISBN-13
9780521773621
eBay Product ID (ePID)
1635204
Product Key Features
Number of Pages
400 Pages
Language
English
Publication Name
Causality : Models, Reasoning, and Inference
Subject
Philosophy & Social Aspects, Physics / General
Publication Year
2000
Type
Textbook
Subject Area
Science
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
31.6 Oz
Item Length
10.4 in
Item Width
7.4 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
LCCN
99-042108
Reviews
"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, "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, "...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., 'The book is extremely well written, and while mathematically precise, provides a thought-provoking study of causality and its implications.' Computing Review, "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, "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, '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, "...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, "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 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
Dewey Edition
21
Illustrated
Yes
Dewey Decimal
122
Table Of Content
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.
Synopsis
Causality offers the first comprehensive coverage of causal analysis in many 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., Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. 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, artificial intelligence, business, epidemiology, social science and economics.
LC Classification Number
BD541 .P43 2000
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