Matching methods for causal inference: A review and a look forward

Research output: Contribution to journalReview articlepeer-review

Abstract

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970s, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.

Original languageEnglish (US)
Pages (from-to)1-21
Number of pages21
JournalStatistical Science
Volume25
Issue number1
DOIs
StatePublished - Feb 2010

Keywords

  • Observational study
  • Propensity scores
  • Subclassification
  • Weighting

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Matching methods for causal inference: A review and a look forward'. Together they form a unique fingerprint.

Cite this