A comparative study of six formal models of causal ascription

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5291 LNAI pp. 47 - 62
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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). © 2008 Springer-Verlag.
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