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.
CITATION STYLE
Benferhat, S., Bonnefon, J. F., Chassy, P., Da Silva Neves, R., Dubois, D., Dupin De Saint-Cyr, F., … Smaoui, S. (2008). A comparative study of six formal models of causal ascription. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5291 LNAI, pp. 47–62). https://doi.org/10.1007/978-3-540-87993-0_6
Mendeley helps you to discover research relevant for your work.