SMU SOE Online Seminar (Sept 11, 2020, 4pm-5.30pm): Treatment Recommendation with Distributional Targets

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TOPIC:

TREATMENT RECOMMENDATION WITH DISTRIBUTIONAL TARGETS

ABSTRACT We study the problem of a decision maker who must provide the best possible treatment recommendation based on an experiment. The desirability of the outcome distribution resulting from the policy recommendation is measured through a functional capturing the distributional characteristic that the decision maker is interested in optimizing. This could be, e.g., its inherent inequality, welfare, level of poverty or its distance to a desired outcome distribution. If the functional of interest is not quasi-convex or if there are constraints, the optimal recommendation may be a mixture of treatments. This vastly expands the set of recommendations that must be considered. We characterize the diﬃculty of the problem by obtaining maximal expected regret lower bounds. Furthermore, we propose two regret-optimal policies. The ﬁrst policy is static and thus applicable irrespectively of the subjects arriving sequentially or not in the course of the experimental phase. The second policy can utilize that subjects arrive sequentially by successively eliminating inferior treatments and thus spends the sampling eﬀort where it is most needed. Keywords: Treatment allocation, best treatment identiﬁcation, statistical decision theory, pure exploration, nonparametric multi-armed bandits. JEL Codes: C18, C22, J68. Click here to view the paper. Click here to view the CV.		This seminar will be held via Zoom. A confirmation email with the Zoom meeting link will be sent the registered email by 10 September 2020.
		PRESENTER Anders Kock University of Oxford
		RESEARCH FIELD Econometrics
		DATE: 11 September 2020 (Friday)
		TIME: 4.00pm - 5.30pm

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