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UID:355@isdm.umontpellier.fr
DTSTART;TZID=Europe/Paris:20250505T140000
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DTSTAMP:20260828T093833Z
URL:https://isdm.umontpellier.fr/events/treatment-allocation-under-uncerta
 in-costs-2/
SUMMARY:Treatment Allocation under Uncertain Costs
DESCRIPTION:Online\n\nMachine Learning in Montpellier\, Theory &amp\; Pract
 ice - Evan Munro (UC Berkeley)\n\nWe consider the problem of learning how 
 to optimally allocate treatments whose cost is uncertain and can vary with
  pre-treatment covariates. This setting may arise in medicine if we need t
 o prioritize access to a scarce resource that different patients would use
  for different amounts of time\, or in marketing if we want to target disc
 ounts whose cost to the company depends on how much the discounts are used
 . Here\, we show that the optimal treatment allocation rule under budget c
 onstraints is a thresholding rule based on priority scores\, and we propos
 e a number of practical methods for learning these priority scores using d
 ata from a randomized trial. Our formal results leverage a statistical con
 nection between our problem and that of learning heterogeneous treatment e
 ffects under endogeneity using an instrumental variable. We find our metho
 d to perform well in a number of empirical evaluations.\n\nMachine Learnin
 g in Montpellier\, Theory &amp\; Practice
ATTACH;FMTTYPE=image/jpeg:https://isdm.umontpellier.fr/wp-content/uploads/
 2026/06/ml-mtp-gC78d5.png
CATEGORIES:ML MTP
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