 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.
<clarity>  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.
 Specifically, we propose a regularization scheme that makes use of auxiliary labelsfor potential shortcut features, which are often available at training time , to enforce conditional independences between the latent factors that determine these labels.