Causality : models, reasoning, and inference
Author(s)
Bibliographic Information
Causality : models, reasoning, and inference
Cambridge University Press, 2009
2nd ed
- : hbk
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Note
Includes bibliographical references (p. 429-452) and indexes
Reprinted with corrections 2013: bibliographical references (p. 429-453)
7th printing 2021: xix, 465 p.
Description and Table of Contents
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.
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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