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

TL;DR

This paper introduces a new algorithm for estimating causal orderings in linear non-Gaussian acyclic models that remains effective even when latent confounders violate some assumptions.

Contribution

The paper presents a robust causal ordering method for LiNGAM that handles latent confounders, improving reliability under assumption violations.

Findings

Effective in artificial data simulations

Robust against latent confounder violations

Improves causal inference accuracy

Abstract

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.