提出一种无需限制混杂变量数量的有限样本方法，精确恢复线性非高斯模型中最稀疏的因果图。
Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
- 利用高阶累积量构建有限样本可计算的稀疏因果图恢复算法
- 在有限样本下优于现有方法，且能处理任意数量的隐变量
- 适用于存在复杂混杂结构的真实数据因果发现任务
在线性非高斯无环模型中存在隐变量时，精确恢复其有向无环图（DAG）仍是难题。尽管LvLiNGAM仅能识别至观测等价类，但每个等价类对应唯一的最稀疏DAG。然而，从有限样本中恢复该最稀疏DAG仍具挑战性。现有方法虽渐近一致，但未提供明确的有限样本恢复程序，也无法处理任意数量的隐变量。本文提出一种无需限制隐变量数量的有限样本方法，可恢复最稀疏DAG。模拟与真实数据实验表明，该方法在有限样本下显著优于现有方法。
原文摘要 · Abstract (English)
Recovering the exact directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM) remains a challenging problem. Although LvLiNGAM is identifiable only up to an observational equivalence class, each equivalence class is characterized by a unique sparsest DAG. Recovering the sparsest DAG from finite samples, however, remains difficult. Although existing methods are asymptotically consistent, they do not provide an explicit finite-sample procedure for recovering the unique sparsest DAG, nor do they handle models with an arbitrary number of latent confounders. In this paper, we propose a finite-sample method for recovering the sparsest DAG without imposing any restriction on the number of latent confounders. Simulation studies and real-data analyses demonstrate that the proposed method achieves superior finite-sample performance compared with existing approaches.
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