Causality : models, reasoning, and inference
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Bibliographic Information
Causality : models, reasoning, and inference
Cambridge University Press, 2001
Repr. with corrections
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Includes bibliographical references (p. 359-373) and indexes
Description and Table of Contents
Description
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.
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 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.
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