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

2012/04/09 by Tatsuya Tashiro, Shohei Shimizu, Tashiro, Tatsuya +6 · 2 citations
Computer Science · Decision Sciences · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (stat.ML) #Multi-Criteria Decision Making #Statistical Methods and Bayesian Inference #stat.ML

paper · pdf · doi:10.48550/arxiv.1204.1795

8 pages, 2 figures

arxiv created 2012/04/09 · openalex publication_date 2012/04/09 · arxiv updated 2012/04/10 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28

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.

Cited by

• Identification of Causal Direction under an Arbitrary Number of Latent Confounders
• ParceLiNGAM: A causal ordering method robust against latent confounders

Related

• ParceLiNGAM: A causal ordering method robust against latent confounders
• A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
• Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
• Joint estimation of linear non-Gaussian acyclic models
• Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding