Causality: Models, Reasoning and Inference

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.

• Causal Inference in Statistics - A Primer
• The Book of Why: The New Science of Cause and Effect
• Counterfactuals and Causal Inference: Methods And Principles For Social Research (Analytical Methods for Social Research)
• Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
• The Book of Why: The New Science of Cause and Effect
• An Introduction to Causal Inference
• Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
• Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)