Discovery of linear acyclic models in the presence of latent classes using ICA mixtures

Causal discovery is the task of finding plausible causal relationships from statistical data. Such methods rely on various assumptions about the data generating process to identify it from uncontrolled observations. We have recently proposed a causal discovery method based on independent component analysis (ICA) called LiNGAM, showing how to completely identify the data generating process under the assumptions of linearity, non-gaussianity, and no hidden variables. In this paper, after briefly recapitulating this approach, we extend the framework to cases where latent (hidden) classes are present. The model identification can be accomplished using ICA mixtures. Experiments confirm the performance of the proposed method.