| Description |
1 online resource (487 pages) |
| 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 |
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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 |
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2.1 INTRODUCTION -- THE BASIC INTUITIONS2.2 THE CAUSAL DISCOVERY FRAMEWORK; 2.3 MODEL PREFERENCE (OCCAM'S RAZOR); 2.4 STABLE DISTRIBUTIONS; 2.5 RECOVERING DAG STRUCTURES; 2.6 RECOVERING LATENT STRUCTURES; 2.7 LOCAL CRITERIA FOR INFERRING CAUSAL RELATIONS; 2.8 NONTEMPORAL CAUSATION AND STATISTICAL TIME; 2.9 CONCLUSIONS; 2.9.1 On Minimality, Markov, and Stability; Relation to the Bayesian Approach; Postscript for the Second Edition; CHAPTER THREE Causal Diagrams and the Identification of Causal Effects; Preface; 3.1 INTRODUCTION; 3.2 INTERVENTION IN MARKOVIAN MODELS |
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3.2.1 Graphs as Models of Interventions3.2.2 Interventions as Variables; 3.2.3 Computing the Effect of Interventions; An Example: Dynamic Process Control; Summary; 3.2.4 Identification of Causal Quantities; 3.3 CONTROLLING CONFOUNDING BIAS; 3.3.1 The Back-Door Criterion; 3.3.2 The Front-Door Criterion; 3.3.3 Example: Smoking and the Genotype Theory; 3.4 A CALCULUS OF INTERVENTION; 3.4.1 Preliminary Notation; 3.4.2 Inference Rules; 3.4.3 Symbolic Derivation of Causal Effects: An Example; 3.4.4 Causal Inference by Surrogate Experiments; 3.5 GRAPHICAL TESTS OF IDENTIFIABILITY |
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3.5.1 Identifying Models3.5.2 Nonidentifying Models; 3.6 DISCUSSION; 3.6.1 Qualifications and Extensions; 3.6.2 Diagrams as a Mathematical Language; 3.6.3 Translation from Graphs to Potential Outcomes; 3.6.4 Relations to Robins's G-Estimation; Personal Remarks and Acknowledgments; Postscript for the Second Edition; Complete identification results; Applications and Critics; Chapter Road Map to the Main Results; CHAPTER FOUR Actions, Plans, and Direct Effects; Preface; 4.1 INTRODUCTION; 4.1.1 Actions, Acts, and Probabilities; 4.1.2 Actions in Decision Analysis; 4.1.3 Actions and Counterfactuals |
| Notes |
4.2 conditional actions and stochastic policies |
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Written by one of the preeminent researchers in the field, this provides a comprehensive exposition of modern analysis of causation |
| Bibliography |
Includes bibliographical references and indexes |
| Notes |
Print version record |
| Subject |
Causation.
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Probabilities.
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probability.
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Causation
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Probabilities
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| Form |
Electronic book
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| LC no. |
99042108 |
| ISBN |
9781139641722 |
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1139641727 |
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9781139638883 |
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1139638882 |
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