ASYMPTOTICS FOR STATISTICAL TREATMENT RULES

• 成果类型: Article
• 署名作者: Hirano, Keisuke; Porter, Jack R.
• 署名单位: University of Arizona; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA6630
• 发表日期: 2009
• 页码: 1683-1701
• 关键词: Ambiguity

摘要

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