Asymptotics for Statistical Treatment Rules
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 hypot...
| Veröffentlicht in: | Econometrica. - Wiley. - 77(2009), 5, Seite 1683-1701 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2009
|
| Zugriff auf das übergeordnete Werk: | Econometrica |
| Schlagworte: | Statistical decision theory treatment assignment minmax minmax regret Bayes rules semiparametric models Mathematics Behavioral sciences Philosophy |
| Zusammenfassung: | 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. |
|---|---|
| ISSN: | 14680262 |