Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants

Ming Cai; Penggang Gao; Hisayuki Hara

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arXiv · 2510.14780

Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants

Ming Cai·Penggang Gao·Hisayuki Hara

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Abstract

This paper addresses the problem of estimating causal directed acyclic graphs in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM). Existing methods assume mutually independent latent confounders or cannot properly handle models with causal relationships among observed variables. We propose a novel algorithm that identifies causal DAGs in LvLiNGAM, allowing causal structures among latent variables, among observed variables, and between the two. The proposed method leverages higher-order cumulants of observed data to identify the causal structure. Extensive simulations and experiments with real-world data demonstrate the validity and practical utility of the proposed algorithm.

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Ming Cai, Penggang Gao, Hisayuki Hara. 2025-10-16. Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants. https://arxiv.org/abs/2510.14780

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