Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
arXiv:2001.01894
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.
arXiv commentAccepted to AISTATS 2020. Camera-ready version in preparation
References in corpus (7)
- Distinguishing cause from effect using observational data: methods and benchmarks
- On the Identifiability of the Post-Nonlinear Causal Model
- Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
- Large-Scale Kernel Methods for Independence Testing
- Causal Discovery Toolbox: Uncover causal relationships in Python
- Causal Inference via Kernel Deviance Measures
- Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models