Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method

We address the problem of distinguishing cause from effect in bivariate setting. Based on recent developments in nonlinear independent component analysis (ICA), we train general nonlinear causal models that are implemented by neural networks and allow nonadditive 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.