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Communication Dans Un Congrès Année : 2008

A Comparative Study of Six Formal Models of Causal Ascription

Rui da Silva Neves
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Résumé

Ascribing causality amounts to determining what elements in a sequence of reported facts can be related in a causal way, on the basis of some knowledge about the course of the world. The paper offers a comparison of a large span of formal models (based on structural equations, non-monotonic consequence relations, trajectory preference relations, identification of violated norms, graphical representations, or connectionism), using a running example taken from a corpus of car accident reports. Interestingly enough, the compared approaches focus on different aspects of the problem by either identifying all the potential causes, or selecting a smaller subset by taking advantages of contextually abnormal facts, or by modeling interventions to get rid of simple correlations. The paper concludes by a general discussion based on a battery of criteria (several of them being proper to AI approaches to causality).

Dates et versions

hal-00710722 , version 1 (21-06-2012)

Identifiants

Citer

Salem Benferhat, Jean-François Bonnefon, Philippe Chassy, Rui da Silva Neves, Didier Dubois, et al.. A Comparative Study of Six Formal Models of Causal Ascription. 2nd International Conference on Scalable Uncertainty Management (SUM 2008), Oct 2008, Naples, Italy. pp.47-62, ⟨10.1007/978-3-540-87993-0_6⟩. ⟨hal-00710722⟩
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