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Statistical treatment choice based on asymmetric minimax regret criteria

Research output: Contribution to journalSpecial issue

Original languageEnglish
Pages (from-to)157-165
Number of pages9
JournalJournal of Econometrics
Volume166
Issue number1
Early online date24 Jun 2011
DOIs
DateE-pub ahead of print - 24 Jun 2011
DatePublished (current) - Jan 2012

Abstract

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.

Additional information

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

    Research areas

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

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at http://www.sciencedirect.com/science/article/pii/S0304407611001266. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 215 KB, PDF-document

    Licence: CC BY-NC-ND

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