提出新方法破解含相关潜变量的线性因果图识别难题
Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulants
- 利用高阶累积量捕捉数据统计特征，突破潜变量独立假设
- 可同时识别观测变量、潜变量及其间的因果关系
- 在模拟与真实数据上验证有效，适合复杂系统因果分析
本文针对存在潜共因的线性非高斯无环模型（LvLiNGAM）中的因果有向无环图估计问题，提出一种新算法。现有方法通常假设潜共因相互独立，或无法处理观测变量间的因果关系。所提方法通过利用观测数据的高阶累积量，能够识别潜变量间、观测变量间以及两者之间的因果结构。大量仿真与真实数据实验表明该算法具有有效性与实际应用价值。
原文摘要 · Abstract (English)
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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