Causality
2nd Edition
Published on 14. September 2009
Book
Hardback
484 pages
978-0-521-89560-6 (ISBN)
Description
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
Reviews / Votes
'Make no mistake about it: this is an important book ... The field has no shortage of lively controversy and divergent opinion, but be that as it may, this is certainly one of the contributions that will bring this material further out of the closet and into the face of the broader statistical community, a move that we should welcome both as consumers and as testers of its utility.' Journal of the American Statistical Association 'Pearl's career has been motivated by problems of artificial intelligence, but the implications of this book are much broader. The distinctions he raises and the mathematical foundation he assembles are critical for every field of scientific endeavor. This updated edition of a modern classic deserves a broad and attentive audience.' H. Van Dyke Parunak, reviews.comMore details
Edition
2nd Revised edition
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Illustrations
7 Tables, unspecified; 58 Halftones, unspecified; 66 Line drawings, unspecified
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 31 mm
Weight
1101 gr
ISBN-13
978-0-521-89560-6 (9780521895606)
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Schweitzer Classification
Other editions
Additional editions
Judea Pearl
Causality
E-Book
02/2013
2nd Edition
Cambridge University Press
€55.49
Available for download
Judea Pearl
Causality
Book
06/2009
2nd Edition
Cambridge University Press
Unfortunately, price unknown
The article will not be published
Previous edition
Judea Pearl
Causality
Book
06/2009
2nd Edition
Cambridge University Press
Unfortunately, price unknown
The article will not be published
Person
Judea Pearl is professor of computer science and statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human reasoning, and philosophy of science. The author of Heuristics and Probabilistic Reasoning, he is a member of the National Academy of Engineering and a Founding Fellow of the American Association for Artificial Intelligence. Dr Pearl is the recipient of the IJCAI Research Excellence Award for 1999, the London School of Economics Lakatos Award for 2001, and the ACM Alan Newell Award for 2004. In 2008, he received the Franklin Medal for computer and cognitive science from the Franklin Institute.
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 social science and economics; 6. Simpson's paradox, confounding, and collapsibility; 7. The logic of structure-based counterfactuals; 8. Imperfect experiments: bounding effects and counterfactuals; 9. Probability of causation: interpretation and identification; 10. The actual cause.