Minimax-regret treatment choice with missing outcome data

Charles F. Manski*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

I use the minimax-regret criterion to study choice between two treatments when some outcomes in the study population are unobservable and the distribution of missing data is unknown. I first assume that observable features of the study population are known and derive the treatment rule that minimizes maximum regret over all possible distributions of missing data. When no treatment is dominant, this rule allocates positive fractions of persons to both treatments. I then assume that the data are a random sample of the study population and show that in some instances, treatment rules that estimate certain point-identified population means by sample averages are finite-sample minimax regret.

Original languageEnglish (US)
Pages (from-to)105-115
Number of pages11
JournalJournal of Econometrics
Volume139
Issue number1
DOIs
StatePublished - Jul 2007

Keywords

  • Partial identification
  • Social choice
  • Statistical decision theory

ASJC Scopus subject areas

  • Economics and Econometrics

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