Interval estimation for treatment effects using propensity score matching

Jennifer Hill, Jerome P. Reiter

Research output: Contribution to journalArticlepeer-review

Abstract

In causal studies without random assignment of treatment, causal effects can be estimated using matched treated and control samples, where matches are obtained using estimated propensity scores. Propensity score matching can reduce bias in treatment effect estimators in cases where the matched samples have overlapping covariate distributions. Despite its application in many applied problems, there is no universally employed approach to interval estimation when using propensity score matching. In this article, we present and evaluate approaches to interval estimation when using propensity score matching.

Original languageEnglish (US)
Pages (from-to)2230-2256
Number of pages27
JournalStatistics in Medicine
Volume25
Issue number13
DOIs
StatePublished - Jul 15 2006

Keywords

  • Bootstrap
  • Matching
  • Observational study
  • Propensity score
  • Randomization

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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