  Shortcut learning, in which models make use of easy-to-represent but unstable
associations, is a major failure mode for robust machine learning. We study a
flexible, causally-motivated approach to training robust predictors by
discouraging the use of specific shortcuts, focusing on a common setting where
a robust predictor could achieve optimal \emph{iid} generalization in
principle, but is overshadowed by a shortcut predictor in practice. Our
approach uses auxiliary labels, typically available at training time, to
enforce conditional independences implied by the causal graph. We show both
theoretically and empirically that causally-motivated regularization schemes
(a) lead to more robust estimators that generalize well under distribution
shift, and (b) have better finite sample efficiency compared to usual
regularization schemes, even when no shortcut is present. Our analysis
highlights important theoretical properties of training techniques commonly
used in the causal inference, fairness, and disentanglement literatures. Our
code is available at
https://github.com/mymakar/causally_motivated_shortcut_removal
