ICML 2018oral41 citations
Detecting non-causal artifacts in multivariate linear regression models
Dominik Janzing, Bernhard Schölkopf
Abstract
We consider linear models where d potential causes X_1,...,X_d are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has ‘generic’ orientation relative to the covariance matrix Sigma_{XX} of X. Using an ICA based model for confounding, we show that both confounding and overfitting yield regression vectors that concentrate mainly in the space of low eigenvalues of Sigma_{XX}.
BibTeX
@InProceedings{pmlr-v80-janzing18a,
title = {Detecting non-causal artifacts in multivariate linear regression models},
author = {Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {2245--2253},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/janzing18a/janzing18a.pdf},
url = {https://proceedings.mlr.press/v80/janzing18a.html},
abstract = {We consider linear models where d potential causes X_1,...,X_d are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has ‘generic’ orientation relative to the covariance matrix Sigma_{XX} of X. Using an ICA based model for confounding, we show that both confounding and overfitting yield regression vectors that concentrate mainly in the space of low eigenvalues of Sigma_{XX}.}
}