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We consider the problem of learning causal models from observational datagenerated by linear non-Gaussian acyclic causal models with latent variables.Without considering the effect of latent variables, one usually infers wrongcausal relationships among the observed variables. Under faithfulnessassumption, we propose a method to check whether there exists a causal pathbetween any two observed variables. From this information, we can obtain thecausal order among them. The next question is then whether or not the causaleffects can be uniquely identified as well. It can be shown that causal effectsamong observed variables cannot be identified uniquely even under theassumptions of faithfulness and non-Gaussianity of exogenous noises. However,we will propose an efficient method to identify the set of all possible causaleffects that are compatible with the observational data. Furthermore, wepresent some structural conditions on the causal graph under which we can learncausal effects among observed variables uniquely. We also provide necessary andsufficient graphical conditions for unique identification of the number ofvariables in the system. Experiments on synthetic data and real-world data showthe effectiveness of our proposed algorithm on learning causal models.

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables