Estimation of causal orders in a linear non-Gaussian acyclic model: a method robust against latent confounders
This addresses a specific issue in causal inference for researchers, but it is incremental as it builds on existing LiNGAM methods.
The paper tackles the problem of estimating causal orders in linear non-Gaussian acyclic models (LiNGAM) by proposing a new algorithm robust against latent confounders, demonstrating its effectiveness on artificial data.
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.