  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.
