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• Preface

• Credits

• PART 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
• References

• 5 Selected Annotated Bibliography by Judea Pearl
• Search and Heuristics
• Bayesian Networks
• Causality
• Causal, Casual, and Curious

• 5 PART II HEURISTICS

• 6 Introduction by Judea Pearl
• References

• 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

• 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

• 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

• PART III PROBABILITIES

• 10 Introduction by Judea Pearl
• References

• 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

• 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

• 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

• 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

• PART IV CAUSALITY 1988-2001

• 15 Introduction by Judea Pearl
• References

• 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

• 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

• 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

• 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

• 20 Direct and Indirect Effects
• Judea Pearl
• Abstract
• 20.1 Introduction
• 20.2 Conceptual Analysis
• 20.3 Formal Analysis
• 20.4 Conclusions
• Acknowledgments
• References

• PART V CAUSALITY 2002-2020

• 21 Introduction by Judea Pearl
• References

• 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

• 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

• 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

• 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

• 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)

• PART VI CONTRIBUTED ARTICLES

• 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

• 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

• 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

• 30 Causal Models and Cognitive Development
• Alison Gopnik
• References

• 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

• 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

• 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

• 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

• 35 A Note of Appreciation
• Azaria Paz
• 35.1 A Magic Square
• 35.2 A Magic Shield of David

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• Editors' Biographies

• Index

• Preface Planning and Autonomous Behavior Classical Planning: Full Information and Deterministic Actions Classical Planning: Variations and Extensions Beyond Classical Planning: Transformations Planning with Sensing: Logical Models MDP Planning: Stochastic Actions and Full Feedback POMDP Planning: Stochastic Actions and Partial Feedback Discussion Bibliography Author's Biography.
• (source: Nielsen Book Data)

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.
(source: Nielsen Book Data)

• Preface Planning and Autonomous Behavior Classical Planning: Full Information and Deterministic Actions Classical Planning: Variations and Extensions Beyond Classical Planning: Transformations Planning with Sensing: Logical Models MDP Planning: Stochastic Actions and Full Feedback POMDP Planning: Stochastic Actions and Partial Feedback Discussion Bibliography Author's Biography.
• (source: Nielsen Book Data)

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.
(source: Nielsen Book Data)

• A system of defeasible inference based on probabilities
• high probabilities and preferential structures
• irrelevance and prioritized preferential structures
• the causal dimension - evidence vs. explanation
• proofs.
• (source: Nielsen Book Data)

Defaults in commonsense reasoning permit the generation of useful predictions in the absence of complete information. However, attempts to represent and reason with defaults in AI run into the problem of spurious arguments, arguments that rely on acceptable defaults but which support unacceptable conclusions. Geffner addresses this problem by analyzing the causal and conditional aspects of default, establishing theoretical limits on the capabilities of probabilistic approaches. He provides new insights into the nature of defaults, and new methods of processing databases containing default expressions. The result is a default-handling system that yields an intuitive behaviour in several domains of interest in AI, including inheritance hierarchies, reasoning about change, general logic programs, and abductive reasoning.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 11th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2008, held in Lisbon, Portugal, in October 2008. The 46 papers presented were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on Knowledge Representation; Planning and Scheduling; Constraint Satisfaction and Optimization; Probabilistic Reasoning; Machine Learning; Multiagent Systems; Natural Language Processing; Intelligent Information Systems and NLP; Robotics; and, Applications.
(source: Nielsen Book Data)