ASYMPTOTICS FOR STATISTICAL TREATMENT RULES

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