  We address the problem of distinguishing cause from effect in bivariate
setting. Based on recent developments in nonlinear independent component
analysis (ICA), we train nonparametrically general nonlinear causal models that
allow non-additive noise. Further, we build an ensemble framework, namely
Causal Mosaic, which models a causal pair by a mixture of nonlinear models. We
compare this method with other recent methods on artificial and real world
benchmark datasets, and our method shows state-of-the-art performance.
