Causality

by Judea Pearl

• ISBN13: 9780521895606
• ISBN10: 052189560X
• Edition: 2nd
• Format: Hardcover
• Copyright: 2009-09-14
• Publisher: Cambridge University Press

Summary

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 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 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.

Table of Contents

Introduction to probabilities, graphs, and causal models

A theory of inferred causation

Causal diagrams and the identification of causal effects

Actions, plans, and direct effects

Causality and structural models in social science and economics

Simpson's paradox, confounding, and collapsibility

The logic of structure-based counterfactuals

Imperfect experiments: bounding effects and counterfactuals

Probability of causation: interpretation and identification

The actual cause

Table of Contents provided by Publisher. All Rights Reserved.