Set-Valued Policy Learning
The paper introduces set-valued policy learning to address situations where multiple treatments yield similar outcomes or no effect. Instead of recommending a single treatment, this approach outputs sets of valuable treatments, where the size of the set shows how uncertain the recommendation is. It develops methods like Greatest Lower Bound and conformal learning to find these robust policies, which are then evaluated using doubly robust estimators.