Before revision:  We show both theoretically and empirically that this causally-motivated regularization scheme yields robust predictors that generalize well both in-distribution and under distribution shifts, and does so with better sample efficiency than standard regularization or weighting approaches .
After revision:  We show both theoretically and empirically that this causally-motivated regularization schemes (a) lead to more robust estimators that generalize well both in-distribution and under distribution shifts, and does so with better sample efficiency than standard regularization or weighting approaches .