We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.

研究在具有潜在变量的线性非高斯无环模型(LiNGAM)中因果效应的通用可识别性问题。针对已知先验的因果图和未知因果图两种情况，对观察变量之间可识别的直接或总因果效应提供了完整的图形特征化，并提出了有效的算法来验证图形条件。最后，提出了重建独立成分分析(RICA)算法的改进版本，该算法可从观测数据中估计因果效应，并通过实验证明了所提方法估计因果效应的有效性。

潜在混杂因素下的LiNGAM模型中的因果效应识别