Causality

Causality

by Pearl, Judea

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Title Statements

  • Full title :Causality.

Edition Statements

  • Publication Date:2009.
  • Edition:2nd ed

Publication Statements

Notes

4.2 CONDITIONAL ACTIONS AND STOCHASTIC POLICIES.

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.

The Physical Object

  • Description:1 online resource (487 pages)

Edition Identifiers

  • ISBN:9781139641722, 1139641727, 1139637800, 9781139637800, 1316088383, 9781316088388, 1139649361, 9781139649360, 0511803168, 9780511803161, 1139638882, 9781139638883, 1299409237, 9781299409231, 1139648403, 9781139648400
  • oclc:ocn956653825
  • LC Classification:BD541
  • Dewey Decimal:122

Table of Contents

Cover; CAUSALITY: Models, Reasoning, and Inference Second Edition; Series Page; Title; Copyright; Dedication; Contents; Preface to the First Edition; Preface to the Second Edition; CHAPTER ONE Introduction to Probabilities, Graphs, and Causal Models; 1.1 INTRODUCTION TO PROBABILITY THEORY; 1.1.1 Why Probabilities?; 1.1.2 Basic Concepts in Probability Theory; 1.1.3 Combining Predictive and Diagnostic Supports; 1.1.4 Random Variables and Expectations; 1.1.5 Conditional Independence and Graphoids; 1.2 GRAPHS AND PROBABILITIES; 1.2.1 Graphical Notation and Terminology; 1.2.2 Bayesian Networks.

1.2.3 The d-Separation Criterion1.2.4 Inference with Bayesian Networks; 1.3 CAUSAL BAYESIAN NETWORKS; 1.3.1 Causal Networks as Oracles for Interventions; 1.3.2 Causal Relationships and Their Stability; 1.4 FUNCTIONAL CAUSAL MODELS; 1.4.1 Structural Equations; 1.4.2 Probabilistic Predictions in Causal Models; 1.4.3 Interventions and Causal Effects in Functional Models; 1.4.4 Counterfactuals in Functional Models; 1.5 CAUSAL VERSUS STATISTICAL TERMINOLOGY; Causal versus Statistical Concepts; Two Mental Barriers to Causal Analysis; CHAPTER TWO A Theory of Inferred Causation; Preface.

2.1 INTRODUCT…

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Subjects

Causation Kausalität Probabilités Probabilities PROBABILITY Science -- Philosophy Wahrscheinlichkeitsrechnung

Writers

Pearl, Judea

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