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

Abstract

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.

Accepted to AISTATS 2020. Camera-ready version in preparation

References in corpus (2)

• On the Identifiability of the Post-Nonlinear Causal Model
• Causal Discovery Toolbox: Uncover causal relationships in Python

Cited by in corpus (1)

• A polynomial-time algorithm for learning nonparametric causal graphs