A comparative study of six formal models of causal ascription

Benferhat, S; Bonnefon, JF; Chassy, P; Da Silva Neves, R; Dubois, D; Dupin De Saint-Cyr, F; Kayser, D; Nouioua, F; Nouioua-Boutouhami, S; Prade, H; Smaoui, S

A comparative study of six formal models of causal ascription

Benferhat, S Bonnefon, JF Chassy, P Da Silva Neves, R Dubois, D Dupin De Saint-Cyr, F Kayser, D Nouioua, F Nouioua-Boutouhami, S Prade, H

Smaoui, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5291 LNAI pp. 47 - 62
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Benferhat, S	en_US
dc.contributor.author	Bonnefon, JF	en_US
dc.contributor.author	Chassy, P	en_US
dc.contributor.author	Da Silva Neves, R	en_US
dc.contributor.author	Dubois, D	en_US
dc.contributor.author	Dupin De Saint-Cyr, F	en_US
dc.contributor.author	Kayser, D	en_US
dc.contributor.author	Nouioua, F	en_US
dc.contributor.author	Nouioua-Boutouhami, S	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.contributor.author	Smaoui, S	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, 5291 LNAI pp. 47 - 62	en_US
dc.identifier.isbn	3540879927	en_US
dc.identifier.isbn	9783540879923	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32818
dc.description.abstract	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.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-540-87993-0_6	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A comparative study of six formal models of causal ascription	en_US
dc.type	Conference Proceeding
utslib.citation.volume	5291 LNAI	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	5291 LNAI	en_US

Abstract:

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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http://hdl.handle.net/10453/32818