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

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

2012-04-09

arxiv.org/abs/1204.1795v1

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Machine Learning

8 pages, 2 figures

Scientific paper

We consider to learn 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.

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