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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. The book will open the way for including causal analysis in the standard curricula of statistics, artificial intelligence, business, epidemiology, social sciences, and economics. Students in these fields will find natural models, simple inferential procedures, and precise mathematical definitions of causal concepts that traditional texts have evaded or made unduly complicated. The first edition of Causality has led to a paradigmatic change in the way that causality is treated in statistics, philosophy, computer science, social science, and economics. Cited in more than 5,000 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 interests to students and professionals in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.Read more
- Presents the first unified account of the various approaches to causation
- Offers simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions, and observations
- Facilitates the incorporation of causal analysis as an integral part of the standard curricula in statistics, artificial intelligence, business, epidemiology, social science, and economics
- The author is the winner of the prestigious Technion Harvey Prize 2012, considered a good predictor of the Nobel Prize, 'in recognition of his foundational work that has touched a multitude of spheres of modern life'
- Winner of the 2011 ACM Turing Award for Transforming Artificial Intelligence
Reviews & endorsements
"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
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"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, Computing Reviews
"Pearl’s book is about probabilistic approaches to causality and it gives, especially, empirical researchers working with observational data an immense aid to their research. It also gives theoretical statisticians something to think about as it raises many issues of estimation for example in respective data generating processes. ... This work of Pearl’s is an invaluable contribution to the current discussion on the topic of causal modeling. As described by the author his main objective of the book is to develop a framework that integrates substantive knowledge including counterfactuals (through new notations and concepts) with statistical data so as to refine the former and to interpret the latter."
Priyantha Wijayatunga, Significance
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- Date Published: September 2009
- format: Hardback
- isbn: 9780521895606
- length: 484 pages
- dimensions: 260 x 185 x 30 mm
- weight: 1.03kg
- contains: 124 b/w illus. 7 tables
- availability: Temporarily unavailable - available from July 2018
Table of Contents
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.
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