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

Pengzhou Wu, Kenji Fukumizu
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1157-1167, 2020.

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 general nonlinear causal models that are implemented by neural networks and 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 this Paper


BibTeX
@InProceedings{pmlr-v108-wu20b, title = {Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method}, author = {Wu, Pengzhou and Fukumizu, Kenji}, booktitle = {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics}, pages = {1157--1167}, year = {2020}, editor = {Chiappa, Silvia and Calandra, Roberto}, volume = {108}, series = {Proceedings of Machine Learning Research}, month = {26--28 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v108/wu20b/wu20b.pdf}, url = {http://proceedings.mlr.press/v108/wu20b.html}, 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 general nonlinear causal models that are implemented by neural networks and 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.} }
Endnote
%0 Conference Paper %T Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method %A Pengzhou Wu %A Kenji Fukumizu %B Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2020 %E Silvia Chiappa %E Roberto Calandra %F pmlr-v108-wu20b %I PMLR %P 1157--1167 %U http://proceedings.mlr.press/v108/wu20b.html %V 108 %X 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 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.
APA
Wu, P. & Fukumizu, K.. (2020). Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:1157-1167 Available from http://proceedings.mlr.press/v108/wu20b.html.

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