Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
ISBN:978-1-4503-9586-1
Pages:
946
Appears In:
ACMACM Books
Bibliometrics

Abstract

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.

prefatory
Preface
February 2022, pp xxv–xxvihttps://doi.org/10.1145/3501714.3501715
prefatory
Credits
February 2022, pp xxvii–xxviiihttps://doi.org/10.1145/3501714.3501716
chapter
Biography of Judea Pearl by Stuart J. Russell
chapter
Turing Award Lecture
chapter
chapter
Selected Annotated Bibliography by Judea Pearl
chapter
Introduction by Judea Pearl
chapter
chapter
chapter
Introduction by Judea Pearl
February 2022, pp 393–398https://doi.org/10.1145/3501714.3501737
chapter
The Tale Wags the DAG
February 2022, pp 557–574https://doi.org/10.1145/3501714.3501744
chapter
Causal Models and Cognitive Development
February 2022, pp 593–604https://doi.org/10.1145/3501714.3501746
chapter
The Causal Foundations of Applied Probability and Statistics
February 2022, pp 605–624https://doi.org/10.1145/3501714.3501747
chapter
Causal Graphs for Missing Data: A Gentle Introduction
February 2022, pp 655–666https://doi.org/10.1145/3501714.3501750
chapter
Causal Models for Dynamical Systems
February 2022, pp 671–690https://doi.org/10.1145/3501714.3501752
chapter
chapter
Causality for Machine Learning
February 2022, pp 765–804https://doi.org/10.1145/3501714.3501755
chapter
Why Did They Do That?
February 2022, pp 805–812https://doi.org/10.1145/3501714.3501756
chapter
Causation: Objective or Subjective?
February 2022, pp 867–888https://doi.org/10.1145/3501714.3501759

Contributors

  • Joseph Halpern
    Cornell University

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