An analytical study on causal induction
Version 2 2024-06-06, 11:39Version 2 2024-06-06, 11:39
Version 1 2014-11-20, 12:32Version 1 2014-11-20, 12:32
conference contribution
posted on 2024-06-06, 11:39 authored by H Dai, S Kenbl-JohnsonAutomatic causal discovery is a challenge research with extraordinary significance in sceintific research and in many real world problems where recovery of causes and effects and their causality relationship is an essential task. This paper firstly introduces the causality and perspectives of causal discovery. Then it provides an anlaysis on the three major approaches that are proposed in the last decades for the automatic discovery of casual models from given data. Afterwards it presents a analysis on the capability and applicability of the different proposed approaches followed by a conclusion on the potentials and the future research. © 2013 IEEE.
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Pagination
908-913Location
Shenyang, ChinaPublisher DOI
Start date
2013-07-23End date
2013-07-25ISBN-13
9781467352536Language
engPublication classification
E Conference publication, E1 Full written paper - refereedCopyright notice
2013, IEEEEditor/Contributor(s)
Chen J, Wang X, Wang L, Sun J, Meng XTitle of proceedings
FSKD 2013 : Proceedings of the Fuzzy Systems and Knowledge Discovery 2013 international conferenceEvent
Fuzzy Systems and Knowledge Discovery. Conference (10th : 2013 : Shenyang, China)Publisher
IEEE Computer SocietyPlace of publication
Piscataway, N.J.Series
Fuzzy Systems and Knowledge Discovery International ConferenceUsage metrics
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