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An analytical study on causal induction

Dai, Honghua and Kenbl-Johnson, Sarah 2013, An analytical study on causal induction, in FSKD 2013 : Proceedings of the Fuzzy Systems and Knowledge Discovery 2013 international conference, IEEE Computer Society, Piscataway, N.J., pp. 908-913, doi: 10.1109/FSKD.2013.6816324.

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Title An analytical study on causal induction
Author(s) Dai, Honghua
Kenbl-Johnson, Sarah
Conference name Fuzzy Systems and Knowledge Discovery. International Conference (10th : 2013 : Shenyang, China)
Conference location Shenyang, China
Conference dates 23-25 Jul. 2013
Title of proceedings FSKD 2013 : Proceedings of the Fuzzy Systems and Knowledge Discovery 2013 international conference
Editor(s) Chen, J.
Wang, X.
Wang, L.
Sun, J.
Meng, X.
Publication date 2013
Conference series Fuzzy Systems and Knowledge Discovery International Conference
Start page 908
End page 913
Total pages 6
Publisher IEEE Computer Society
Place of publication Piscataway, N.J.
Keyword(s) Causal Induction
Causality
data mining
Machine learning
Summary 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.
ISBN 9781467352536
Language eng
DOI 10.1109/FSKD.2013.6816324
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30067585

Document type: Conference Paper
Collection: School of Information Technology
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