Causes and Explanations: A Structural-Model Approach: Part 1: Causes

By Joseph Y. Halpern, Judea Pearl. 2013

81 citations recorded by the source corpus.

We propose a new definition of actual causes, using structural equations to model counterfactuals.We show that the definitions yield a plausible and elegant account ofcausation that handles well examples which have caused problems forother definitions and resolves major difficulties in the traditionalaccount. In a companion paper, we show how the definition of causality can beused to give an elegant definition of (causal) explanation.

Subfields: Artificial Intelligence, History and Philosophy of Science

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Citations in the corpus

Structural equations and causation (2007, background): “First, in the more general treatment (given in Pearl & Halpern 2005, for example), the sort of thing that can be an effect is any proposition whose truth is determined by the distribution of values for variables in the model; the sorts of things that can be causes are arbitrary conjunctions of…”
Causes and explanations: A structural-model approach (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.”
Debugging Machine Learning Tasks (2016, methodology): “An application of the structural equations framework is Halpern and Pearl’s deﬁnition of actual causation [26] (known as the HP-Deﬁnition).”
Causes and Explanations: A Structural-Model Approach. Part I: Causes (2000, methodology): “In a preliminary version of this paper [Halpern and Pearl 2001], we required only that AC2(b) hold for ~ W .”
Sequences of Mechanisms for Causal Reasoning in Artificial Intelligence (2013, unknown): “In particular, [Halpern and Pearl(2005)] present a deﬁnition of causal explanation which uses the concept of actual cause from [Halpern and Pearl(2001)], based on functional causal models of [Pearl(2000)], to produce sets of variables which are deemed to be possible explanations for some evidence.”