Bootstrap Inference of Matching Estimators for Average Treatment Effects

Taisuke Otsu, Yoshiyasu Rai

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


It is known that the naive bootstrap is not asymptotically valid for a matching estimator of the average treatment effect with a fixed number of matches. In this article, we propose asymptotically valid inference methods for matching estimators based on the weighted bootstrap. The key is to construct bootstrap counterparts by resampling based on certain linear forms of the estimators. Our weighted bootstrap is applicable for the matching estimators of both the average treatment effect and its counterpart for the treated population. Also, by incorporating a bias correction method in Abadie and Imbens (2011), our method can be asymptotically valid even for matching based on a vector of covariates. A simulation study indicates that the weighted bootstrap method is favorably comparable with the asymptotic normal approximation. As an empirical illustration, we apply the proposed method to the National Supported Work data. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)1720-1732
Number of pages13
JournalJournal of the American Statistical Association
Issue number520
Publication statusPublished - 2017 Oct 2
Externally publishedYes


  • Bootstrap
  • Matching
  • Treatment effect

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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