An analytical study on causal induction


Autoria(s): Dai, Honghua; Kenbl-Johnson, Sarah
Contribuinte(s)

Chen, J.

Wang, X.

Wang, L.

Sun, J.

Meng, X.

Data(s)

01/01/2013

Resumo

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.

Identificador

http://hdl.handle.net/10536/DRO/DU:30067585

Idioma(s)

eng

Publicador

IEEE Computer Society

Relação

http://dro.deakin.edu.au/eserv/DU:30067585/dai-ananalyticalstudy-2013.pdf

http://dro.deakin.edu.au/eserv/DU:30067585/dai-ananalyticalstudy-evid-2013.pdf

http://www.dx.doi.org/10.1109/FSKD.2013.6816324

Direitos

2013, IEEE

Palavras-Chave #Causal Induction #Causality #data mining #Machine learning
Tipo

Conference Paper