Asymptotics for Statistical Treatment Rules
在光滑参数和半参数模型中，为统计处理规则开发了渐近最优性理论，使用实验极限框架推导出在平均和极小化最大风险准则下渐近最优的处理分配规则。
This paper develops asymptotic optimality theory for statistical treatment rules in smooth parametric and semiparametric models. Manski (2000, 2002, 2004) and Dehejia (2005) have argued that the problem of choosing treatments to maximize social welfare is distinct from the point estimation and hypothesis testing problems usually considered in the treatment effects literature, and advocate formal analysis of decision procedures that map empirical data into treatment choices. We develop large-sample approximations to statistical treatment assignment problems using the limits of experiments framework. We then consider some different loss functions and derive treatment assignment rules that are asymptotically optimal under average and minmax risk criteria. Copyright 2009 The Econometric Society.