用高阶累积量解决潜变量模型中的因果效应识别难题
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
- 基于高阶累积量识别潜变量因果效应
- 单个代理变量或工具变量即可实现准确估计
- 适合处理有潜混淆的线性系统因果推断
本文研究在潜变量线性非高斯无环模型（lvLiNGAM）中，利用高阶累积量进行因果效应识别，重点解决两类在潜混淆存在下具有挑战性的场景：(1) 仅有一个可能影响处理变量的代理变量；(2) 工具变量数量少于处理变量的欠指定情况。证明了在单一代理变量或工具变量条件下因果效应可识别，并提出了相应的估计方法。实验结果表明，该方法在准确性和鲁棒性上优于现有方法，深化了对存在潜混淆的线性系统中因果推断的理论与实践理解。
原文摘要 · Abstract (English)
This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups that are challenging in the presence of latent confounding: (1) a single proxy variable that may causally influence the treatment and (2) underspecified instrumental variable cases where fewer instruments exist than treatments. We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods. Experimental results demonstrate the accuracy and robustness of our approaches compared to existing methods, advancing the theoretical and practical understanding of causal inference in linear systems with latent confounders.
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