Causality

Каузальность
Judea Pearl
2009-09-14

causal inferencecausalitycounterfactualsprobabilistic causationstructural causal models
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.
1
Causality is presented as a rigorous mathematical theory applicable across statistics, AI, economics, philosophy, cognitive science, and health/social sciences.
2
Simple mathematical tools are devised to study relationships between causal connections and statistical associations.
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The book unifies probabilistic, manipulative, counterfactual, and structural approaches to causation into a coherent framework.
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The revised edition clarifies difficult issues, answers reader questions, and surveys recent advances in causal research.
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The work has had substantial scientific impact, being cited in over 2,100 publications and influencing statistical thinking.

Mathematical theory and formal frameworks of causation (probabilistic, manipulative, counterfactual, and structural approaches)

Unified analysis and mathematical tools for studying relationships between causal connections and statistical associations, including methods for formalizing causation and addressing conceptual issues

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Publication Date
2009-09-14
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Authors
Judea Pearl
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