Detecting Non-Causal Artifacts in Multivariate Linear Regression Models
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.
Cite
Text
Janzing and Schölkopf. "Detecting Non-Causal Artifacts in Multivariate Linear Regression Models." International Conference on Machine Learning, 2018.Markdown
[Janzing and Schölkopf. "Detecting Non-Causal Artifacts in Multivariate Linear Regression Models." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/janzing2018icml-detecting/)BibTeX
@inproceedings{janzing2018icml-detecting,
title = {{Detecting Non-Causal Artifacts in Multivariate Linear Regression Models}},
author = {Janzing, Dominik and Schölkopf, Bernhard},
booktitle = {International Conference on Machine Learning},
year = {2018},
pages = {2245-2253},
volume = {80},
url = {https://mlanthology.org/icml/2018/janzing2018icml-detecting/}
}