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An analytical study on causal induction

Version 2 2024-06-06, 11:39
Version 1 2014-11-20, 12:32
conference contribution
posted on 2024-06-06, 11:39 authored by H Dai, S Kenbl-Johnson
Automatic 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.

History

Pagination

908-913

Location

Shenyang, China

Start date

2013-07-23

End date

2013-07-25

ISBN-13

9781467352536

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2013, IEEE

Editor/Contributor(s)

Chen J, Wang X, Wang L, Sun J, Meng X

Title of proceedings

FSKD 2013 : Proceedings of the Fuzzy Systems and Knowledge Discovery 2013 international conference

Event

Fuzzy Systems and Knowledge Discovery. Conference (10th : 2013 : Shenyang, China)

Publisher

IEEE Computer Society

Place of publication

Piscataway, N.J.

Series

Fuzzy Systems and Knowledge Discovery International Conference

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