Causes and explanations: A structural-model approach

By Joseph Y. Halpern, Judea Pearl. 2000

419 citations recorded by the source corpus.

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Citations in the corpus

Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “ee of necessity’ of causes. An event that is fully responsible outcome for an event is a necessary cause. Chockler and Halpern [29] modied the structural equation model proposed by Halpern and Pearl [58] (see Section 2.1.1) to dene responsibility of an outcome. Informally, they dene the responsibili”
Causes and Explanations: A Structural-Model Approach. Part I: Causes (2000, methodology): “Here we give a definition of actual causality based on the language of structural equations; in a companion paper ([Halpern and Pearl 2001]; see also the full paper [Halpern and Pearl 2000]), we give a definition of (causal) explanation using the definition of causality.”
Responsibility and blame: a structural-model approach (2003, methodology): “Finally, in the case of Suzy and Billy, even though Suzy is the only cause of the bottle shattering, Suzy’s degree of responsibility is 1/2, while Billy’s is 0.”
Explainability in Deep Reinforcement Learning (2020, background): “Structural causal models [66] represent the world using random variables, some of which might have causal relationships, which can be described thanks to a set of structural equations.”
Structural equations and causation (2007, methodology): “2 For representative treatments of causation along these lines, see Pearl (2000), Hitchcock (2001), and Halpern and Pearl (2005).”
Intelligible Artificial Intelligence (2018, background): “The fact that event C causes E is best understood relative to an imagined counterfactual scenario, where absent C, E would not have occurred; furthermore, C should be minimal — an intuition known to early scientists, such as William of Occam and formalized by Halpern and Pearl [13].”
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).”
Complexity results for structure-based causality (2001, methodology): “We consider the notions of necessary and possible causality, and the sophisticated notions of weak and actual causality by Halpern and Pearl [2000].”
Causal Responsibility and Counterfactuals (2013, methodology): “We are now in a position to consider a definition of actual causation based on the structural model approach (Halpern & Pearl, 2005).”
Causes and Explanations: A Structural-Model Approach: Part 1: Causes (2013, methodology): “Here we give a definition of actual causality based on the language of structural equations; in a companion paper ([Halpern and Pearl 2001]; see also the full paper [Halpern and Pearl 2000]), we give a definition of (causal) explana tion using the definition of causality.”
Probabilities of causation: Bounds and identification (2000, background): “Such consider ations will be the subject of future investigation (See [Halpern and Pearl, 2000]).”
Degrees of Causation (2009, background): “(12)Halpern and Pearl (2005) provide a fully fledged formal structure of the NESS test that takes”
Intuitive Theories of Mind: A Rational Approach to False Belief (2005, unknown): “(See Halpern and Pearl (2001) for a related approach.”
Causes and explanations in the structural-model approach: Tractable cases (2002, methodology): “[21] J. Pearl.”
Counterfactual Theories of Causation (2001, background): “More recently, counterfactual-based accounts that partly reject conceptual reductionism have received a good deal of attention, particularly causal modeling accounts such as Pearl (2000), 31 Halpern and Pearl (2005), 32 and Hitchcock (2001).”