Causality by Judea Pearl
Models, Reasoning, and Inference
Introduces a formal framework for reasoning about cause and effect using graphical models, structural equations, and the do-operator, explaining how to distinguish correlation from causation, identify confounding, and compute the effects of interventions. It presents criteria for identifiability, tools for mediation analysis and policy evaluation, and a logic of counterfactuals, all unified by causal diagrams and do-calculus, with examples spanning social sciences, medicine, and AI.
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- Published
- 2000
- Nationality
- American
- Length
- Moderate
- Pages
- 384-464
- Original Language
- English
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- Alternate Titles
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