2013/10/24 by Shohei Shimizu, Kenneth Bollen, Kenneth A. Bollen +2
Chemistry · Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Blind Source Separation Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Spectroscopy and Chemometric Analyses #stat.ML
paper · pdf · doi:10.48550/arxiv.1310.6778
21 pages, 4 figures. A revised version was accepted at Journal of Machine Learning Research
openalex publication_date 2013/10/24 · arxiv created 2014/05/20 · arxiv updated 2014/05/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.