Estimation of causal orders in a linear non-gaussian acyclic model: a method robust against latent confounders

Authors:
Tatsuya Tashiro;Shohei Shimizu;Aapo Hyvärinen;Takashi Washio
Affiliations:
The Institute of Scientific and Industrial Research, Osaka University, Japan;The Institute of Scientific and Industrial Research, Osaka University, Japan;Dept. of Mathematics and Statistics, Dept. of Computer Science / HIIT, University of Helsinki, Finland;The Institute of Scientific and Industrial Research, Osaka University, Japan
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

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Abstract

We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually are violated. In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. We demonstrate the effectiveness of our method using artificial data.