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.

翻译：本研究探讨了含潜在变量的线性非高斯无环模型（LiNGAM）中因果效应的泛化可识别性问题。我们主要在两种设定下考虑该问题：当因果图结构已知时，以及当因果图结构未知时。在这两种设定下，我们均给出了观测变量间可识别的直接或总因果效应的完整图条件刻画。此外，我们提出了高效算法以验证这些图条件。最后，我们改进了重构独立成分分析（RICA）算法，使其能够在给定因果图的条件下从观测数据中估计因果效应。实验结果表明，所提方法在因果效应估计方面具有显著有效性。