UID:
almahu_9949497825302882
Format:
1 PDF (xxviii, 916 pages)
,
LuaTEX
Edition:
First Edition
ISBN:
3501714
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9781450395892
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9781450395885
Series Statement:
ACM books, #36
Note:
Preface
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Credits
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PART I INTRODUCTION
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1 Biography of Judea Pearl by Stuart J. Russell -- References
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2 Turing Award Lecture -- References
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3 Interview by Martin Ford -- References
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4 An Interview with Ron Wassertein on How The Book of Why Transforms Statistics -- References
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5 Selected Annotated Bibliography by Judea Pearl -- Search and Heuristics -- Bayesian Networks -- Causality -- Causal, Casual, and Curious
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5 PART II HEURISTICS
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6 Introduction by Judea Pearl -- References
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7 Asymptotic Properties of Minimax Trees and Game-Searching Procedures -- Judea Pearl -- 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
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8 The Solution for the Branching Factor of the Alpha-Beta Pruning Algorithm and its Optimality -- Judea Pearl -- 8.1 Introduction -- 8.2 Analysis -- 8.3 Conclusions -- References
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9 On the Discovery and Generation of Certain Heuristics -- Judea Pearl -- Abstract -- 9.1 Introduction: Typical Uses of Heuristics -- 9.2 Mechanical Generation of Admissible Heuristics -- 9.3 Can a Program Tell an Easy Problem When It Sees One? -- 9.4 Conclusions -- References
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PART III PROBABILITIES
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10 Introduction by Judea Pearl -- References
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11 Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach -- Judea Pearl -- 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
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12 Fusion, Propagation, and Structuring in Belief Networks -- Judea Pearl -- Abstract -- 12.1 Introduction -- 12.2 Fusion and Propagation -- 12.3 Structuring Causal Trees -- 12.A Appendix A. Derivation of the Updating Rules for Singly Connected Networks -- 12.B Appendix B. Conditions for Star-decomposability -- Acknowledgments -- References
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13 GRAPHOIDS: Graph-Based Logic for Reasoning about Relevance Relations Or When Would x Tell You More about y If You Already Know z? -- Judea Pearl and Azaria Paz -- Abstract -- 13.1 Introduction -- 13.2 Probabilistic Dependencies and their Graphical Representation -- 13.3 GRAPHOIDS -- 13.4 Special Graphoids and Open Problems -- 13.5 Conclusions -- References
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14 14 System Z: A Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning -- Judea Pearl -- 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
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PART IV CAUSALITY 1988-2001
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15 Introduction by Judea Pearl -- References
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16 Equivalence and Synthesis of Causal Models -- TS Verma and Judea Pearl -- 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
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17 Probabilistic Evaluation of Counterfactual Queries -- Alexander Balke and Judea Pearl -- 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
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18 Causal Diagrams for Empirical Research (With Discussions) -- Judea Pearl -- Summary -- Some key words -- 18.1 Introduction -- 18.2 Graphical Models and the Manipulative Account of Causation -- 18.3 Controlling Confounding Bias -- 18.4 A Calculus of Intervention -- 18.5 Graphical Tests of Identifiability -- 18.6 Discussion -- Acknowledgments -- 18.A Appendix -- 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.VII Discussion of 'Causal Diagrams for Empirical Research' by J. Pearl -- 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.X Rejoinder to Discussions of 'Causal Diagrams for Empirical Research' -- Additional References
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19 Probabilities of Causation: Three Counterfactual Interpretations and Their Identification -- Judea Pearl -- Abstract -- 19.1 Introduction -- 19.2 Structural Model Semantics (A Review) -- 19.3 Necessary and Sufficient Causes: Conditions of Identification -- 19.4 Examples and Applications -- 19.5 Identification in Non-Monotonic Models -- 19.6 From Necessity and Sufficiency to "Actual Cause" -- 19.7 Conclusion -- 19.A Appendix: The Empirical Content of Counterfactuals -- References
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20 Direct and Indirect Effects -- Judea Pearl -- Abstract -- 20.1 Introduction -- 20.2 Conceptual Analysis -- 20.3 Formal Analysis -- 20.4 Conclusions -- Acknowledgments -- References
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PART V CAUSALITY 2002-2020
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21 Introduction by Judea Pearl -- References
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22 Comment : Understanding Simpson's Paradox -- Judea Pearl -- 22.1 The History -- 22.2 A Paradox Resolved -- 22.3 Armistead's Critique -- 22.4 Conclusions -- References
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23 Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data -- Karthika Mohan and Judea Pearl -- Abstract -- 23.1 Introduction -- 23.2 Missingness Graph and Recoverability -- 23.3 Recovering Probabilistic Queries by Sequential Factorization -- 23.4 Recoverability in the Absence of an Admissible Sequence -- 23.5 Non-recoverability Criteria for Joint and Conditional Distributions -- 23.6 Recovering Causal Queries -- 23.7 Attrition -- 23.8 Related Work -- 23.9 Conclusion -- Acknowledgments -- References -- 23.A Appendix
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24 Recovering from Selection Bias in Causal and Statistical Inference -- Elias Bareinboim, Jin Tian and Judea Pearl -- Abstract -- 24.1 Introduction -- 24.2 Recoverability without External Data -- 24.3 Recoverability with External Data -- 24.4 Recoverability of Causal Effects -- 24.5 Conclusions -- Acknowledgments -- References
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25 External Validity: From Do-Calculus to Transportability Across -- Judea Pearl and Elias Bareinboim -- Abstract -- Key words and phrases -- 25.1 Introduction: Threats vs. Assumptions Populations -- 25.2 Preliminaries: The Logical Foundations of Causal Inference -- 25.3 Inference Across Populations: Motivating Examples -- 25.4 Formalizing Transportability -- 25.5 Transportability of Causal Effects-A Graphical Criterion -- 25.6 Conclusions -- 25.AAppendix -- Acknowledgments -- References
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26 Detecting Latent Heterogeneity -- Judea Pearl -- Abstract -- Keywords -- 26.1 Introduction -- 26.2 Covariate-Induced Heterogeneity -- 26.3 Latent Heterogeneity between the Treated and Untreated -- 26.4 Three Ways of Detecting Heterogeneity -- 26.5 Example: Heterogeneity in Recruitment -- 26.6 Conclusions -- Acknowledgments -- Declaration of Conflicting Interests -- Funding -- References -- Author Biography -- 26.A Appendix A (An Extreme Case of Latent Heterogeneity) -- 26.B Appendix B (Assessing Heterogeneity in Structural Equation Models)
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PART VI CONTRIBUTED ARTICLES
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27 On Pearl's Hierarchy and the Foundations of Causal Inference -- Elias Bareinboim, Juan D. Correa, Duligur Ibeling and Thomas Icard -- Abstract -- 27.1 Introduction -- 27.2 Structural Causal Models and the Causal Hierarchy -- 27.3 Pearl Hierarchy-A Logical Perspective -- 27.4 Pearl Hierarchy-A Graphical Perspective -- 27.5 Conclusions -- Acknowledgments -- References
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28 The Tale Wags the DAG -- Philip Dawid -- Abstract -- 28.1 Introduction -- 28.2 The Ladder of Causation -- 28.3 Ground Level: Syntax -- 28.4 Rung 1: Seeing -- 28.5 Rung 2: Doing -- 28.6 Rung 3: Imagining -- 28.7 Conclusion -- References
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29 Instrumental Variables with Treatment-induced Selection: Exact Bias -- Felix Elwert and Elan Segarra -- 29.1 Introduction -- 29.2 Causal Graphs -- 29.3 Instrumental Variables -- 29.4 Selection Bias in IV: Qualitative Analysis -- 29.5 Selection Bias in IV: Quantitative Analysis -- 29.6 Conclusion -- 29.A Appendix -- References
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30 Causal Models and Cognitive Development -- Alison Gopnik -- References
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31 The Causal Foundations of Applied Probability and Statistics -- Sander Greenland -- Abstract -- 31.1 Introduction: Scientific Inference is a Branch of Causality Theory -- 31.2 Causality is Central Even for Purely Descriptive Goals -- 31.3 The Strength of Probabilistic Independence Demands Physical Independence -- 31.4 The Superconducting Supercollider of Selection -- 31.5 Data and Algorithms are Causes of Reported Results -- 31.6 Getting Causality into Statistics by Putting Statistics into Causal Terms from the Start -- 31.7 Causation in 20th-century Statistics -- 31.8 Causal Analysis versus Traditional Statistical Analysis -- 31.9 Relating Causality to Traditional Statistical Philosophies and "Objective" Statistics --31.10 Discussion -- 31.11 Conclusion -- 31.A Appendix -- Acknowledgments -- References
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32 Pearl on Actual Causation -- Christopher Hitchcock -- Abstract -- 32.1 Introduction -- 32.2 Actual Causation -- 32.3 Causal Models and But-for Causation -- 32.4 Pre-emption and Lewis -- 32.5 Intransitivity and Overdetermination -- 32.6 Pearl's Definitions of Actual Causation -- 32.7 Pearl's Achievement -- References
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33 Causal Diagram and Social Science Research -- Kosuke Imai -- 33.1 Graphical Causal Models and Social Science Research -- 33.2 Two Applications of Graphical Causal Models -- 33.3 The Future of Causal Research in the Social Sciences -- References
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34 Causal Graphs for Missing Data: A Gentle Introduction -- Karthika Mohan -- 34.1 Introduction -- 34.2 Missingness Graphs -- 34.3 Recoverability -- 34.4 Testability -- References
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35 A Note of Appreciation -- Azaria Paz -- 35.1 A Magic Square -- 35.2 A Magic Shield of David
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36 Causal Models for Dynamical Systems -- Jonas Peters, Stefan Bauer and Niklas Pfister -- Abstract -- 36.1 Introduction -- 36.2 Chemical Reaction Networks and ODEs -- 36.3 Causal Kinetic Models - 36.4 Challenges in Causal Inference for ODE-based Systems -- 36.5 From Invariance to Causality and Generalizability -- 36.6 Conclusions -- Acknowledgments -- References
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37 Probabilistic Programming Languages: Independent Choices and Deterministic Systems -- David Poole and Frank Wood -- 37.1 Probabilistic Models and Deterministic Systems -- 37.2 Possible Worlds Semantics -- 37.3 Inference -- 37.4 Learning -- 37.5 Other Issues -- 37.6 Causal Models -- 37.7 Some Pivotal References -- 37.8 Conclusion -- References
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38 An Interventionist Approach to Mediation Analysis -- James M. Robins, Thomas S. Richardson and Ilya Shpitser -- 38.1 Introduction -- 38.2 Approaches to Mediation Based on Counterfactuals Defined in Terms of the Mediator: The CDE and PDE -- 38.3 Interventionist Theory of Mediation -- 38.4 Path-Specific Counterfactuals -- 38.5 Conclusion -- Acknowledgments -- 38.A Appendix -- References
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39 Causality for Machine Learning -- Bernhard Schölkopf -- Abstract -- 39.1 Introduction -- 39.2 The Mechanization of Information Processing -- 39.3 From Statistical to Causal Models -- 39.4 Levels of Causal Modeling -- 39.5 Independent Causal Mechanisms -- 39.6 Cause-Effect Discovery -- 39.7 Half-sibling Regression and Exoplanet Detection -- 39.8 Invariance, Robustness, and Semi-supervised Learning -- 39.9 Causal Representation Learning -- 39.10 Personal Notes and Conclusion -- Acknowledgments -- References
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40 Why Did They Do That? -- Ross Shachter and David Heckerman -- Abstract -- 40.1 Introduction -- 40.2 Some Examples -- 40.3 Back to the Garden of Eden -- 40.4 Decision Theory and Decision Analysis -- 40.5 Back Again in the Garden of Eden -- 40.6 Conclusion: God's Decision -- References
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41 Multivariate Counterfactual Systems and Causal Graphical Models -- Ilya Shpitser, Thomas S. Richardson and James M. Robins -- 41.1 Introduction -- 41.2 Graphs, Non-parametric Structural Equation Models, and the g-/do Operator -- 41.3 The Potential Outcomes Calculus and Identification -- 41.4 Identification in Hidden Variable Causal Models -- 41.5 Conclusion -- Acknowledgments -- 41.A Appendix -- References
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42 Causal Bayes Nets as Psychological Theory -- Steven A. Sloman -- Abstract -- 42.1 The Human Conception of Causality -- 42.2 Core Properties -- 42.3 The Broader Perspective: The Community of Knowledge -- 42.4 Collective Causal Models -- 42.5 Conclusion -- Acknowledgments -- References
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43 Causation: Objective or Subjective? -- Wolfgang Spohn -- Abstract -- 43.1 Causation: A Bunch of Attitudes -- 43.2 The Model Relativity of Causation -- 43.3 Laws -- 43.4 Probability -- Acknowledgments -- References
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Editors' Biographies
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Index
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Also available in print.
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Mode of access: World Wide Web
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System requirements: Adobe Acrobat Reader
Additional Edition:
Print version: ISBN 9781450395861
Additional Edition:
ISBN 9781450395878
Language:
English
Keywords:
Electronic books.
URL:
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