Definition and Learning of Constrained Policies

par M. Antoine Chambaz (Université Paris Cité)

jeudi 11 juin 2026, 11:00 → 12:15 Europe/Paris

Auditorium 5 (Toulouse School of Economics)

Abstract : A medical policy can help personalize treatment recommendations based on patients' characteristics. Classical definitions of such policies are often grounded in a causal framework involving a single clinical outcome. In the common setting where several outcomes must be considered simultaneously, these policies typically neglect the risk of adverse
events. I will present PLUC (Policy Learning Under Constraint), a framework for defining and learning policies that explicitly incorporates one or more constraints.
This work is the result of a collaboration with Laura Fuentes-Vicente, Mathieu Even, Gaëlle Dormion, and Julie Josse.
The corresponding manuscript, entitled "Policy learning under constraint: Maximizing a primary outcome while controlling an adverse event," is available here: https://hal.science/hal-05482399v1.