Statistical treatment choice based on asymmetric minimax regret criteria

Aleksey Tetenov*

*Corresponding author for this work

Research output: Contribution to journalSpecial issue (Academic Journal)peer-review

41 Citations (Scopus)
588 Downloads (Pure)


This paper studies the problem of treatment choice between a status quo treatment with a known outcome distribution and an innovation whose outcomes are observed only in a finite sample. I evaluate statistical decision rules, which are functions that map sample outcomes into the planner's treatment choice for the population, based on regret, which is the expected welfare loss due to assigning inferior treatments. I extend previous work started by Manski (2004) that applied the minimax regret criterion to treatment choice problems by considering decision criteria that asymmetrically treat Type I regret (due to mistakenly choosing an inferior new treatment) and Type II regret (due to mistakenly rejecting a superior innovation) and derive exact finite sample solutions to these problems for experiments with normal, Bernoulli and bounded distributions of outcomes. The paper also evaluates the properties of treatment choice and sample size selection based on classical hypothesis tests and power calculations in terms of regret.

Original languageEnglish
Pages (from-to)157-165
Number of pages9
JournalJournal of Econometrics
Issue number1
Early online date24 Jun 2011
Publication statusPublished - Jan 2012

Bibliographical note

Issue: Annals Issue on "Identification and Decisions'', in Honor of Chuck Manski's 60th Birthday


  • Hypothesis testing
  • Loss aversion
  • Statistical decision theory
  • Treatment effects


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