most citedParceLiNGAM: A causal ordering method robust against latent confounders

1 citations · 1 across the 2 of their papers we have counts for

collaborators
Showing stat.MLShow all

2 papers · 1 filter

stat.ML2013★ 1 cited

ParceLiNGAM: A causal ordering method robust against latent confounders

Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen +1

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…

stat.ML2012

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

Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen +1

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…