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Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence

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  • Zhong Zhao

    (China Center for Economic Research, Peking University)

Abstract

We compare propensity-score matching methods with covariatematching estimators. We first discuss the data requirements of propensity-score matching estimators and covariate matching estimators. Then we propose two new matching metrics incorporating the treatment outcome information and participation indicator information, and discuss the motivations of different metrics. Next we study the small-sample properties of propensity-score matching versus covariate matching estimators, and of different matching metrics, through Monte Carlo experiments. Through a series of simulations, we provide some guidance to practitioners on how to choose among different matching estimators and matching metrics. 2004 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
  • Handle: RePEc:tpr:restat:v:86:y:2004:i:1:p:91-107
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