Probabilistic and Causal Inference : The Works of Judea Pearl / Hector Geffner, Rita Dechter, and Joseph Halpern (editors).

Format:

Book

Contributor:

Geffner, Hector, editor.

Dechter, Rita, editor.

Halpern, Joseph Y., 1953- editor.

Series:

ACM books ; Number 36.

ACM books ; Number 36

Language:

English

Subjects (All):

Artificial intelligence.

Probabilities.

Physical Description:

xxvii, 916 s. ill.

Edition:

First edition.

Place of Publication:

[Place of publication not identified] : Association for Computing Machinery, [2022]

Summary:

Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl's work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

Contents:

Intro

Probabilistic and Causal Inference: The Works of Judea Pearl

Contents

Preface

Credits

I INTRODUCTION

1 Biography of Judea Pearl by Stuart J. Russell

References

2 Turing Award Lecture

3 Interview by Martin Ford

4 An Interview with Ron Wassertein on How The Book of Why Transforms Statistics

5 Selected Annotated Bibliography by Judea Pearl

Search and Heuristics

Bayesian Networks

Causality

Causal, Casual, and Curious

II HEURISTICS

6 Introduction by Judea Pearl

7 Asymptotic Properties of Minimax Trees and Game-Searching Procedures

Abstract

7.1 The Probability of Winning a Standard h-level Game Tree with Random WIN Positions

7.2 Game Trees with an Arbitrary Distribution of Terminal Values

7.3 The Mean Complexity of Solving (h, d, P0)-game

7.4 Solving, Testing, and Evaluating Game Trees

7.5 Test and, if Necessary, Evaluate-The SCOUT Algorithm

7.6 Analysis of SCOUT's Expected Performance

7.7 On the Branching Factor of the ALPHA-BETA (α-β) procedure

8 The Solution for the Branching Factor of the Alpha-Beta Pruning Algorithm and its Optimality

8.1 Introduction

8.1.1 Informal Description of the α-β Procedure

8.1.2 Previous Analytical Results

8.2 Analysis

8.2.1 An Integral Formula for Nn,d

8.2.2 Evaluation of Rα-β

8.3 Conclusions

9 On the Discovery and Generation of Certain Heuristics

9.1 Introduction: Typical Uses of Heuristics

9.1.1 The Traveling Salesman Problem (TSP)

9.1.2 Some Properties of Heuristics

9.1.3 Where do these Heuristics Come from?

9.2 Mechanical Generation of Admissible Heuristics

9.3 Can a Program Tell an Easy Problem When It Sees One?

9.4 Conclusions

9.4.1 Bibliographical and Historical Remarks

References.

III PROBABILITIES

10 Introduction by Judea Pearl

11 Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach

11.1 Introduction

11.2 Definitions and Nomenclature

11.3 Structural Assumptions

11.4 Combining Top and Bottom Evidences

11.5 Propagation of Information Through the Network

11.6 A Token Game Illustration

11.7 Properties of the Updating Scheme

11.8 A Summary of Proofs

11.9 Conclusions

12 Fusion, Propagation, and Structuring in Belief Networks

12.1 Introduction

12.1.1 Belief Networks

12.1.2 Conditional Independence and Graph Separability

12.1.3 An Outline and Summary of Results

12.2 Fusion and Propagation

12.2.1 Autonomous Propagation as a Computational Paradigm

12.2.2 Belief Propagation in Trees

12.2.2.1 Data Fusion

12.2.2.2 Propagation Mechanism

12.2.2.3 Illustrating the Flow of Belief

12.2.2.4 Properties of the Updating Scheme

12.2.3 Propagation in Singly Connected Networks

12.2.3.1 Fusion Equations

12.2.3.2 Propagation Equation

12.2.4 Summary and Extensions for Multiply Connected Networks

12.3 Structuring Causal Trees

12.3.1 Causality, Conditional Independence, and Tree Architecture

12.3.2 Problem Definition and Nomenclature

12.3.3 Star-Decomposable Triplets

12.3.4 A Tree-Reconstruction Procedure

12.3.5 Conclusions and Open Questions

12.A Appendix A. Derivation of the Updating Rules for Singly Connected Networks

12.A.1 Updating BEL

12.A.2 Updating π

12.A.3 Updating λ

12.B Appendix B. Conditions for Star-decomposability

Acknowledgments

13 GRAPHOIDS: Graph-Based Logic for Reasoning about Relevance Relations Or When Would x Tell You More about y If You Already Know z?

13.1 Introduction.

13.2 Probabilistic Dependencies and their Graphical Representation

13.3 GRAPHOIDS

13.4 Special Graphoids and Open Problems

13.4.1 Graph-induced Graphoids

13.4.2 Probabilistic Graphoids

13.4.3 Correlational Graphoids

13.5 Conclusions

14 System Z: A Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning

14.1 Description

14.2 Consequence Relations

14.3 Illustrations

14.4 The Maximum Entropy Approach

14.5 Conditional Entailment

14.6 Conclusions

14.I Appendix I: Uniqueness of The Minimal Ranking Function

14.II Appendix II: Rational Monotony of Admissible Rankings

IV CAUSALITY 1988-2001

15 Introduction by Judea Pearl

16 Equivalence and Synthesis of Causal Models

16.1 Introduction

16.2 Patterns of Causal Models

16.3 Embedded Causal Models

16.4 Applications to the Synthesis of Causal Models

IC-Algorithm (Inductive Causation)

17 Probabilistic Evaluation of Counterfactual Queries

17.1 Introduction

17.2 Notation

17.3 Party Example

17.4 Probabilistic vs. Functional Specification

17.5 Evaluating Counterfactual Queries

17.6 Party Again

17.7 Special Case: Linear-Normal Models

17.8 Conclusion

18 Causal Diagrams for Empirical Research (With Discussions)

Summary

Some key words

18.1 Introduction

18.2 Graphical Models and the Manipulative Account of Causation

18.2.1 Graphs and Conditional Independence

18.2.2 Graphs as Models of Interventions

18.3 Controlling Confounding Bias

18.3.1 The Back-Door Criterion

18.3.2 The Front-Door Criteria

18.4 A Calculus of Intervention

18.4.1 General

18.4.2 Preliminary Notation

18.4.3 Inference Rules.

18.4.4 Symbolic Derivation of Causal Effects: An Example

18.4.5 Causal Inference by Surrogate Experiments

18.5 Graphical Tests of Identifiability

18.5.1 General

18.5.2 Identifying Models

18.5.3 Nonidentifying Models

18.6 Discussion

18.A Appendix

Proof of Theorem 18.3

18.I Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.II Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.III Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.IV Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.V Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.VI Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.VI.A Introduction

18.VI.B Task 1

18.VI.B.1 General

18.VI.B.2 A Causal Model

18.VI.B.3 Relationship with Pearl's Work

18.VI.C Task 2

18.VII Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.VII.A Successful and Unsuccessful Causal Inference: Some Examples

18.VII.B Warranted Inferences

18.VIII Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.IX Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl

18.IX.A Introduction

18.IX.B Ignorability and the Back-Door Criterion

18.X Rejoinder to Discussions of 'Causal Diagrams for Empirical Research'

18.X.A General

18.X.B Graphs, Structural Equations and Counterfactuals

18.X.C The Equivalence of Counterfactual and Structural Analyses

18.X.D Practical Versus Hypothetical Interventions

18.X.E Intervention as Conditionalisation

18.X.F Testing Versus using Assumptions

18.X.G Causation Versus Dependence

18.X.H Exemplifying Modelling Errors

18.X.I The Myth of Dangerous Graphs

Additional References.

19 Probabilities of Causation: Three Counterfactual Interpretations and Their Identification

19.1 Introduction

19.2 Structural Model Semantics (A Review)

19.2.1 Definitions: Causal Models, Actions and Counterfactuals

19.2.2 Examples

19.2.3 Relation to Lewis' Counterfactuals

19.2.4 Relation to Probabilistic Causality

19.2.5 Relation to Neyman-Rubin Model

19.3 Necessary and Sufficient Causes: Conditions of Identification

19.3.1 Definitions, Notations, and Basic Relationships

19.3.2 Bounds and Basic Relationships under Exogeneity

19.3.3 Identifiability under Monotonicity and Exogeneity

19.3.4 Identifiability under Monotonicity and Non-Exogeneity

19.4 Examples and Applications

19.4.1 Example 1: Betting against a Fair Coin

19.4.2 Example 2: The Firing Squad

19.4.3 Example 3: The Effect of Radiation on Leukemia

19.4.4 Example 4: Legal Responsibility from Experimental and Nonexperimental Data

19.5 Identification in Non-Monotonic Models

19.6 From Necessity and Sufficiency to "Actual Cause"

19.6.1 The Role of Structural Information

19.6.2 Singular Sufficient Causes

19.6.3 Example: The Desert Traveler (after P. Suppes)

19.6.3.1 Necessity and Sufficiency Ignoring Internal Structure

19.6.3.2 Sufficiency and Necessity given Forensic Reports

19.6.3.3 Necessity Given Forensic Reports

19.7 Conclusion

19.A Appendix: The Empirical Content of Counterfactuals

20 Direct and Indirect Effects

20.1 Introduction

20.2 Conceptual Analysis

20.2.1 Direct versus Total Effects

20.2.2 Descriptive versus Prescriptive Interpretation

20.2.3 Policy Implications of the Descriptive Interpretation

20.2.4 Descriptive Interpretation of Indirect Effects

20.3 Formal Analysis

20.3.1 Notation

20.3.2 Controlled Direct Effects (review).

20.3.3 Natural Direct Effects: Formulation.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781450395892

1450395899

OCLC:

1319037364