Causes and Explanations: A Structural-Model Approach. Part I: Causes

By Joseph Y. Halpern, Judea Pearl. 2000

903 citations recorded by the source corpus.

We propose a new definition of actual causes, using structural equations to model counterfactuals. We show that the definition yields a plausible and elegant account of causation that handles well examples which have caused problems for other definitions and resolves major difficulties in the traditional account. 1. Introduction2. Causal models: a review 2.1Causal models 2.2Syntax and semantics3. The definition of cause4. Examples5. A more refined definition6. Discussion AAppendix: Some Technical Issues A.1The active causal process A.2A closer look at AC2(b) A.3Causality with infinitely many variables A.4Causality in nonrecursive models Introduction Causal models: a review 2.1Causal models 2.2Syntax and semantics 2.1Causal models 2.2Syntax and semantics The definition of cause Examples A more refined definition Discussion AAppendix: Some Technical Issues A.1The active causal process A.2A closer look at AC2(b) A.3Causality with infinitely many variables A.4Causality in nonrecursive models

Subfields: Artificial Intelligence, History and Philosophy of Science, Statistics and Probability

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “…to, and perhaps even the same as causal reasoning, since explanations often refer to causation or causal mechanisms, and causal analyses are believed to have explanatory value (Cohen, 2008; Halpern and Pearl, 2005a,b; Krull and Anderson, 1997; Pearl, 1988; Pearl and Mackenzie, 2018; Trout, 2002).”
Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution (2018, background): “For example, how likely it is that Joe’s swimming exercise was a necessary (or sufficient) cause of Joe’s death (Pearl, 2015a; Halpern and Pearl, 2005).”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “the philosophy of science [81, 82, 108–110, 184], but also in the psychology of human agents [35, 124, 125], and benefits from strong technical foundations [19, 78].”
Varieties of Causal Intervention (2004, methodology): “, it may have been useful in guiding intuitions in Halpern and Pearl’s study of token causation, [6, 7]), it clearly will not do for a general analysis.”
Causal Explanation Under Indeterminism: A Sampling Approach (2016, methodology): “Halpern and Pearl (2005a; 2005b) introduced a method (henceforth “HP”) for explanation that links structural equation models (SEMs) and counterfactuals.”
Spreading the blame: The allocation of responsibility amongst multiple agents. (2010, methodology): “Their model is cast in the framework of causal models to capture the counterfactual dependencies between sets of events (Halpern & Pearl, 2005).”
Intuitive Theories (2020, methodology): “Within the class of dependency theories, the CBN framework has been employed to yield formal definitions of actual causation (Halpern & Pearl, 2005; Hitchcock, 2009) and, more recently, these accounts have been extended to give graded causal judgments by considering default states of variables…”
A Regularity Theoretic Approach to Actual Causation (2013, background): “Many of these theories operate within a broadly counterfactual framework that draws on structural equations (cf. e.g. Hitchcock 2001; 2007; Woodward 2003; Halpern and Pearl 2005; Halpern 2008; Halpern and Hitchcock 2010).”
Causal superseding. (2015, background): “The causal superseding effect demonstrated across these four studies is both an exciting discovery and something that has been in legal records for over a century (Hart & Honoré, 1985).”
Degrees of Causation (2009, background): “See Halpern and Pearl (2005).”
From counterfactual simulation to causal judgment (2014, background): “However, in line with Wolff's (2007) force dynamics model, we acknowledge that people's intuitive theories are often much richer than what can be expressed in terms of the formal accounts of counterfactual reasoning to date (e.g. Halpern & Pearl, 2005).”
An Anatomy of Moral Responsibility (2012, background): “Halpern and Pearl (2005) provide a fully fledged formal structure of the NESS test that takes into account some of the problems that the NESS test faces.”
Explainability in Deep Reinforcement Learning (2020, background): “As this achieved goal diﬀers from the initial goal ("Pick the red ball"), the goal mapper relabels the episode, and both trajectories are appended to the replay buﬀer.”
Structural equations and causation (2007, methodology): “(Halpern & Pearl 2005, p. REF, with minor relettering) I think the discussion here has gone off the rails.”