标题： 通过高阶累积量对具有依赖潜变量的线性DAG进行因果发现 显示英文标题

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

摘要： 本文解决了在存在潜在混杂因素的线性非高斯无环模型（LvLiNGAM）中估计因果有向无环图的问题。 现有方法假设潜在混杂因素相互独立，或者无法正确处理观测变量之间存在因果关系的模型。 我们提出了一种新算法，能够在LvLiNGAM中识别因果DAG，允许潜在变量之间、观测变量之间以及两者之间的因果结构。 所提出的方法利用观测数据的高阶累积量来识别因果结构。 大量模拟和真实数据实验证明了所提出算法的有效性和实用性。 显示英文摘要

摘要： 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.