  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.
