 A key challenge to learning such risk-invariant predictors is shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle . We propose a flexible, causally-motivated approach to address this challenge. Specifically, we propose a regularization scheme that makes use of auxiliary labelsfor potential shortcut features, which are often available at training time . Drawing on the causal structure of the problem, we enforce a conditional independence between the representation used to predict the main label and the auxiliary labels.
<coherence>  A key challenge to learning such risk-invariant predictors is shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle . We propose a flexible, causally-motivated approach to address this challenge. Specifically, we propose a regularization scheme that makes use of auxiliary labelsfor potential shortcut features, which are often available at training time . Drawing on the causal structure of the problem, we enforce a conditional independence between the representation used to predict the main label and the auxiliary labels.
 A key challenge to learning such risk-invariant predictors is shortcut learning, or the tendency for models to rely on spurious correlations in practice, even when a predictor based on shift-invariant features could achieve optimal i.i.d generalization in principle , but instead it relies on spurious correlations or shortcuts in practice. Our approach uses auxiliary labels, typically available at training time . Drawing on the causal structure of the problem, we enforce a conditional independence between the representation used to predict the main label and the auxiliary labels.