Ranking and Selection with Covariates for Personalized Decision Making

Haihui Shen (), L. Jeff Hong () and Xiaowei Zhang ()
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Haihui Shen: Sino-US Global Logistics Institute, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China
L. Jeff Hong: School of Management and School of Data Science, Fudan University, Shanghai 200433, China
Xiaowei Zhang: HKU Business School, The University of Hong Kong, Pokfulam Road, Hong Kong SAR

INFORMS Journal on Computing, 2021, vol. 33, issue 4, 1500-1519

Abstract: We consider a problem of ranking and selection via simulation in the context of personalized decision making, in which the best alternative is not universal, but varies as a function of some observable covariates. The goal of ranking and selection with covariates (R&S-C) is to use simulation samples to obtain a selection policy that specifies the best alternative with a certain statistical guarantee for subsequent individuals upon observing their covariates. A linear model is proposed to capture the relationship between the mean performance of an alternative and the covariates. Under the indifference-zone formulation, we develop two-stage procedures for both homoscedastic and heteroscedastic simulation errors, respectively, and prove their statistical validity in terms of average probability of correct selection. We also generalize the well-known slippage configuration and prove that the generalized slippage configuration is the least favorable configuration for our procedures. Extensive numerical experiments are conducted to investigate the performance of the proposed procedures, the experimental design issue, and the robustness to the linearity assumption. Finally, we demonstrate the usefulness of R&S-C via a case study of selecting the best treatment regimen in the prevention of esophageal cancer. We find that by leveraging disease-related personal information, R&S-C can substantially improve patients’ expected quality-adjusted life years by providing a patient-specific treatment regimen.

Keywords: ranking and selection; covariates; probability of correct selection; least favorable configuration; experimental design (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:4:p:1500-1519

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