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ORCIDProbabilistic and Causal Inference, 2022
- Hector Geffner, Rina Dechter, Joseph Y. Halpern:
Probabilistic and Causal Inference: The Works of Judea Pearl. ACM Books 36, ACM 2022, ISBN 978-1-4503-9586-1 - Preface.
- Credits.
Introduction
- Stuart J. Russell:
Biography of Judea Pearl. 1-10 - Judea Pearl:
Turing Award Lecture. 11-28 - Interview by Martin Ford. 29-42
- An Interview with Ron Wassertein on How The Book of Why Transforms Statistics. 43-48
- Selected Annotated Bibliography by Judea Pearl. 49-56
Heuristics
- Judea Pearl:
Heuristics - Introduction. 57-60 - Judea Pearl:
Asymptotic Properties of Minimax Trees and Game-Searching Procedures. 61-90 - Judea Pearl:
The Solution for the Branching Factor of the Alpha-Beta Pruning Algorithm and its Optimality. 91-102 - Judea Pearl:
On the Discovery and Generation of Certain Heuristics. 103-122
Probabilities
- Judea Pearl:
Probabilities - Introduction. 123-128 - Judea Pearl:
Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach. 129-138 - Judea Pearl:
Fusion, Propagation, and Structuring in Belief Networks. 139-188 - Judea Pearl, Azaria Paz:
GRAPHOIDS: Graph-Based Logic for Reasoning about Relevance Relations OrWhen Would x Tell You More about y If You Already Know z? 189-200 - Judea Pearl:
System Z: A Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning. 201-214
Causality 1988-2001
- Judea Pearl:
Causality 1988-2001 - Introduction. 215-220 - Thomas Verma, Judea Pearl:
Equivalence and Synthesis of Causal Models. 221-236 - Alexander Balke, Judea Pearl:
Probabilistic Evaluation of Counterfactual Queries. 237-254 - Judea Pearl:
Causal Diagrams for Empirical Research (With Discussions). 255-316 - Judea Pearl:
Probabilities of Causation: Three Counterfactual Interpretations and Their Identification. 317-372 - Judea Pearl:
Direct and Indirect Effects. 373-392
Causality 2002-2020
- Judea Pearl:
Causality 2002-2020 - Introduction. 393-398 - Judea Pearl:
Comment: Understanding Simpson's Paradox. 399-412 - Karthika Mohan, Judea Pearl:
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data. 413-432 - Elias Bareinboim, Jin Tian, Judea Pearl:
Recovering from Selection Bias in Causal and Statistical Inference. 433-450 - Judea Pearl, Elias Bareinboim:
External Validity: From Do-Calculus to Transportability Across Populations. 451-482 - Judea Pearl:
Detecting Latent Heterogeneity. 483-506
Contributed Articles
- Elias Bareinboim, Juan D. Correa
, Duligur Ibeling, Thomas Icard:
On Pearl's Hierarchy and the Foundations of Causal Inference. 507-556 - A. Philip Dawid:
The Tale Wags the DAG. 557-574 - Felix Elwert, Elan Segarra:
Instrumental Variables with Treatment-induced Selection: Exact Bias Results. 575-592 - Alison Gopnik:
Causal Models and Cognitive Development. 593-604 - Sander Greenland:
The Causal Foundations of Applied Probability and Statistics. 605-624 - Christopher Hitchcock:
Pearl on Actual Causation. 625-646 - Kosuke Imai:
Causal Diagram and Social Science Research. 647-654 - Karthika Mohan:
Causal Graphs for Missing Data: A Gentle Introduction. 655-666 - Azaria Paz:
A Note of Appreciation. 667-670 - Jonas Peters, Stefan Bauer, Niklas Pfister:
Causal Models for Dynamical Systems. 671-690 - David Poole, Frank Wood:
Probabilistic Programming Languages: Independent Choices and Deterministic Systems. 691-712 - James M. Robins, Thomas S. Richardson, Ilya Shpitser:
An Interventionist Approach to Mediation Analysis. 713-764 - Bernhard Schölkopf:
Causality for Machine Learning. 765-804 - Ross D. Shachter, David Heckerman:
Why Did They Do That? 805-812 - Ilya Shpitser, Thomas S. Richardson, James M. Robins:
Multivariate Counterfactual Systems and Causal Graphical Models. 813-852 - Steven A. Sloman:
Causal Bayes Nets as Psychological Theory. 853-866 - Wolfgang Spohn:
Causation: Objective or Subjective? 867-888 - Editor's Biographies/Index. 889-893
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