 Here, we consider the problem of learning a predictor whose risk across such shifts is invariant. 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 .
<clarity>  Here, we consider the problem of learning a predictor whose risk across such shifts is invariant. 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 .
 Here, we study a flexible, causally-motivated approach to enforcing such invariances, paying special attention to 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 .