Probabilistic and Causal Inference : The Works of Judea Pearl.
Hector. Geffner
Bok Engelsk 2022
| Omfang | xxvii, 916 s. : ill.
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| Opplysninger | 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 -- References -- 3 Interview by Martin Ford -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 9 On the Discovery and Generation of Certain Heuristics -- Abstract -- 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.. - 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 -- References -- 14 System Z: A Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning -- Abstract -- 14.1 Description -- 14.2 Consequence Relations -- 14.3 Illustrations -- 14.4 The Maximum Entropy Approach -- 14.5 Conditional Entailment -- 14.6 Conclusions -- Acknowledgments -- 14.I Appendix I: Uniqueness of The Minimal Ranking Function -- 14.II Appendix II: Rational Monotony of Admissible Rankings -- References -- IV CAUSALITY 1988-2001 -- 15 Introduction by Judea Pearl -- References -- 16 Equivalence and Synthesis of Causal Models -- Abstract -- 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) -- Acknowledgments -- References -- 17 Probabilistic Evaluation of Counterfactual Queries -- Abstract -- 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 -- Acknowledgments -- References -- 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 -- Acknowledgments -- 18.A Appendix -- Proof of Theorem 18.3 -- References -- 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 -- Abstract -- 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 -- References -- 20 Direct and Indirect Effects -- Abstract -- 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.. - III PROBABILITIES -- 10 Introduction by Judea Pearl -- References -- 11 Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach -- Abstract -- 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 -- References -- 12 Fusion, Propagation, and Structuring in Belief Networks -- Abstract -- 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 -- References -- 13 GRAPHOIDS: Graph-Based Logic for Reasoning about Relevance Relations Or When Would x Tell You More about y If You Already Know z? -- Abstract -- 13.1 Introduction.. - 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.
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| Emner | |
| ISBN | 9781450395892
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| ISBN(galt) |
