ParceLiNGAM: a causal ordering method robust against latent confounders.

Abstract:

:We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM. Several 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 are violated. In this letter, 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 unaffected by latent confounders. We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data.

journal_name

Neural Comput

journal_title

Neural computation

authors

Tashiro T,Shimizu S,Hyvärinen A,Washio T

doi

10.1162/NECO_a_00533

subject

Has Abstract

pub_date

2014-01-01 00:00:00

pages

57-83

issue

1

eissn

0899-7667

issn

1530-888X

journal_volume

26

pub_type

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