Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants
Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Cai, Ming, Gao, Penggang, Hara, Hisayuki
Institution: Kyoto University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7092286346
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