- 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? Probabilistic and Causal Inference 2022: 189-200 - Jonas Peters, Stefan Bauer, Niklas Pfister:
Causal Models for Dynamical Systems. Probabilistic and Causal Inference 2022: 671-690 - James M. Robins, Thomas S. Richardson, Ilya Shpitser:
An Interventionist Approach to Mediation Analysis. Probabilistic and Causal Inference 2022: 713-764 - Stuart J. Russell:
Biography of Judea Pearl. Probabilistic and Causal Inference 2022: 1-10 - Bernhard Schölkopf:
Causality for Machine Learning. Probabilistic and Causal Inference 2022: 765-804 - Ross D. Shachter, David Heckerman:
Why Did They Do That? Probabilistic and Causal Inference 2022: 805-812 - Ilya Shpitser, Thomas S. Richardson, James M. Robins:
Multivariate Counterfactual Systems and Causal Graphical Models. Probabilistic and Causal Inference 2022: 813-852 - Steven A. Sloman:
Causal Bayes Nets as Psychological Theory. Probabilistic and Causal Inference 2022: 853-866 - Wolfgang Spohn:
Causation: Objective or Subjective? Probabilistic and Causal Inference 2022: 867-888 - Thomas Verma, Judea Pearl:
Equivalence and Synthesis of Causal Models. Probabilistic and Causal Inference 2022: 221-236 - Preface. Probabilistic and Causal Inference 2022
- Credits. Probabilistic and Causal Inference 2022
- Interview by Martin Ford. Probabilistic and Causal Inference 2022: 29-42
- An Interview with Ron Wassertein on How The Book of Why Transforms Statistics. Probabilistic and Causal Inference 2022: 43-48
- Selected Annotated Bibliography by Judea Pearl. Probabilistic and Causal Inference 2022: 49-56
- Editor's Biographies/Index. Probabilistic and Causal Inference 2022: 889-893
- 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 [contents]