Ranking and Selection with Covariates for Personalized Decision Making

7 Oct 2017 Haihui Shen L. Jeff Hong Xiaowei Zhang

We consider a problem of ranking and selection via simulation in the context of personalized decision making, where 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 certain statistical guarantee for subsequent individuals upon observing their covariates... (read more)

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