Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data

Joel L. Horowitz*, Charles F. Manski

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

165 Scopus citations

Abstract

Analysis of randomized experiments with missing covariate and outcome data is problematic, because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This article shows how population parameters can be bounded without making untestable distributional assumptions. Bounds are also derived under the assumption that covariate data are missing completely at random. In each case the bounds are sharp; they exhaust all of the information available given the data and the maintained assumptions. The bounds are illustrated with applications to data obtained from a clinical trial and data relating family structure to the probability that a youth graduates from high school.

Original languageEnglish (US)
Pages (from-to)77-84
Number of pages8
JournalJournal of the American Statistical Association
Volume95
Issue number449
DOIs
StatePublished - Mar 1 2000

Keywords

  • Attrition
  • Bounds
  • Identification

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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