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Activity Number: 6 - Recent Advance of Nonparametric and Semiparametric Techniques with Complex Data Structure
Type: Invited
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #326569
Title: Quantile-Optimal Treatment Regimes
Author(s): Lan Wang* and Yu Zhou and Rui Song and Ben Sherwood
Companies: University of Minnesota and University of Minnesota and North Carolina State University and University of Kansas
Keywords: quantile criterion; optimal treatment regime; precision medicine

Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in areas such as precision medicine, government policies and active labor market interventions. In the current literature, the optimal treatment regime is usually defined as the one that maximizes the average benefit in the potential population. This paper studies a general framework for estimating the quantile-optimal treatment regime, which is of importance in many real-world applications. Given a collection of treatment regimes, we consider robust estimation of the quantile-optimal treatment regime, which does not require the analyst to specify an outcome regression model. We propose an alternative formulation of the estimator as a solution of an optimization problem with an estimated nuisance parameter. We derive theory involving a nonstandard convergence rate and a non-normal limiting distribution.

Authors who are presenting talks have a * after their name.

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