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

arXiv: https://arxiv.org/abs/2510.14780 · HTML: https://mlsanity.com/paper/2510.14780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2025.

## Abstract (arXiv)

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.