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

• URL: http://arxiv.org/abs/2510.14780v1
• Date: Thu, 16 Oct 2025 15:15:20 GMT
• Title: Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants
• Authors: Ming Cai, Penggang Gao, Hisayuki Hara,
• Abstract summary: Existing methods assume mutually independent latent confounders or cannot properly handle models with causal relationships among observed variables.<n>We propose a novel algorithm that identifies causal DAGs in LvLiNGAM, allowing causal structures among latent variables, among observed variables, and between the two.
• Score: 7.808674222118538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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