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SUMMARY:Definition and Learning of Constrained Policies
DTSTART:20260611T090000Z
DTEND:20260611T101500Z
DTSTAMP:20260614T024000Z
UID:indico-event-16403@indico.math.cnrs.fr
DESCRIPTION:Speakers: Antoine Chambaz (Université Paris Cité)\n\nAbstrac
 t : A medical policy can help personalize treatment recommendations based 
 on patients' characteristics. Classical definitions of such policies are o
 ften grounded in a causal framework involving a single clinical outcome. I
 n the common setting where several outcomes must be considered simultaneou
 sly\, these policies typically neglect the risk of adverseevents. I will p
 resent 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\, M
 athieu Even\, Gaëlle Dormion\, and Julie Josse.The corresponding manuscri
 pt\, entitled "Policy learning under constraint: Maximizing a primary outc
 ome while controlling an adverse event\," is available here: https://hal.
 science/hal-05482399v1.\n\nhttps://indico.math.cnrs.fr/event/16403/
LOCATION:Auditorium 5 (Toulouse School of Economics)
URL:https://indico.math.cnrs.fr/event/16403/
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