Asymptotics for statistical treatment rules

Keisuke Hirano, Jack R. Porter

Research output: Contribution to journalArticlepeer-review

79 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1683-1701
Number of pages19
Issue number5
StatePublished - Sep 2009
Externally publishedYes


  • Bayes rules
  • Minmax
  • Minmax regret
  • Semiparametric models
  • Statistical decision theory
  • Treatment assignment

ASJC Scopus subject areas

  • Economics and Econometrics


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