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The statistical decision theory pioneered by (Wald, Statistical decision functions, Wiley, 1950) has used state-dependent mean loss (risk) to measure the performance of statistical decision functions across potential samples. We think it evident that evaluation of performance should respect...
We consider inference on optimal treatment assignments. Our methods allow inference on the treatment assignment rule that would be optimal given knowledge of the population treatment effect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the...
Statistical treatment rules map data into treatment choices. Optimal treatment rules maximize social welfare. Although some finite sample results exist, it is generally difficult to prove that a particular treatment rule is optimal. This paper develops asymptotic and numerical results on...
This study considers the treatment choice problem when the outcome variable is binary. We focus on statistical treatment rules that plug in fitted values from a nonparametric kernel regression, and show that the maximum regret can be calculated by maximizing over two parameters. Using this...
The Maximin and Choquet expected utility theories guide decision-making under ambiguity. We apply them to hypothesis testing in incomplete models. We consider a statistical risk function that uses a prior probability to incorporate parameter uncertainty and a belief function to reflect the...
We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data. We contribute to the literature by anchoring our finite-sample analysis on mean square regret, a decision criterion advocated by Kitagawa et al. in (2022) "Treatment Choice...
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