Deconfounding and Causal Regularization for Stability and External Validity
Methodology
2020-08-17 v1 Machine Learning
Abstract
We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint. We describe how simple and user-friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In this sense, we provide additional thoughts to the issue on concept drift, raised by Efron (2020), when the data generating distribution is changing.
Cite
@article{arxiv.2008.06234,
title = {Deconfounding and Causal Regularization for Stability and External Validity},
author = {Peter Bühlmann and Domagoj Ćevid},
journal= {arXiv preprint arXiv:2008.06234},
year = {2020}
}
Comments
23 pages, 7 figures