 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 .
<clarity>  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 .
 We show both theoretically and empirically that 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 .