ASYMPTOTICS FOR STATISTICAL TREATMENT RULES
ASYMPTOTICS FOR STATISTICAL TREATMENT RULES
复制标题
DOI：
10.3982/ecta6630
复制
发表时间：
2009-09-01
期刊：
影响因子：
6.1
通讯作者：
Porter, Jack R.
中科院分区：
文献类型：
--
作者：
Hirano, Keisuke;Porter, Jack R.
Manski（2000,2002,2004）和Dehejia（2005）认为，选择治疗方法以最大化社会福利的问题不同于通常在治疗效果文献中考虑的点估计和假设检验问题，并主张正式分析：将经验数据映射到治疗选择的决策程序我们使用实验框架的极限开发了统计治疗分配问题的大样本近似值，然后考虑一些不同的损失函数并推导出在平均和最小最大风险标准下渐近最优的治疗分配规则
This paper develops asymptotic optimality theory for statistical treatment rides 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