  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. The key idea is to detect latent confounders
by testing independence between estimated external influences and find subsets
(parcels) that include variables that are not affected by latent confounders.
We demonstrate the effectiveness of our method using artificial data and
simulated brain imaging data.
