Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
Machine Learning
2021-01-19 v1 Machine Learning
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.
Cite
@article{arxiv.2001.01894,
title = {Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method},
author = {Pengzhou Wu and Kenji Fukumizu},
journal= {arXiv preprint arXiv:2001.01894},
year = {2021}
}
Comments
Accepted to AISTATS 2020. Camera-ready version in preparation