Bayesian estimation of possible causal direction in the presence of latent confounders using a linear non-Gaussian acyclic structural equation model with individual-specific effects
This addresses a key limitation in causal inference for researchers, as existing methods often fail when latent confounders exist, though it appears incremental by extending prior models to include such confounders.
The paper tackles the problem of estimating causal direction between two observed variables when latent confounders are present, by proposing a linear non-Gaussian acyclic structural equation model with individual-specific effects and an empirical Bayesian approach, demonstrating effectiveness on artificial and real-world data.
We consider learning the possible causal direction of two observed variables in the presence of latent confounding variables. Several existing methods have been shown to consistently estimate causal direction assuming linear or some type of nonlinear relationship and no latent confounders. However, the estimation results could be distorted if either assumption is actually violated. In this paper, we first propose a new linear non-Gaussian acyclic structural equation model with individual-specific effects that allows latent confounders to be considered. We then propose an empirical Bayesian approach for estimating possible causal direction using the new model. We demonstrate the effectiveness of our method using artificial and real-world data.