Constrained Classification and Policy Learning
The research investigates when surrogate loss methods remain consistent when classifiers are restricted by constraints, especially when standard assumptions about classifier richness fail. It finds that hinge losses are necessary for consistency under prediction set constraints, but consistency is not guaranteed if the functional form is also constrained. This framework is applied to causal policy learning by linking risk minimization to welfare maximization.