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.

翻译：本文研究具有潜在混杂因子的线性非高斯无环模型中的因果有向无环图估计问题。现有方法通常假设潜在混杂因子相互独立，或无法正确处理观测变量间存在因果关系的模型。我们提出一种新颖算法，能够在允许潜在变量间、观测变量间以及两类变量间均存在因果结构的线性非高斯无环模型中识别因果有向无环图。该方法利用观测数据的高阶累积量来识别因果结构。大量仿真实验和真实数据实验验证了所提算法的有效性与实用价值。