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 Computjournal_title
Neural computationauthors
Tashiro T,Shimizu S,Hyvärinen A,Washio Tdoi
10.1162/NECO_a_00533subject
Has Abstractpub_date
2014-01-01 00:00:00pages
57-83issue
1eissn
0899-7667issn
1530-888Xjournal_volume
26pub_type
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