Bootstrap inference of matching estimators for average treatment effects

Otsu, Taisuke and Rai, Yoshiyasu (2017) Bootstrap inference of matching estimators for average treatment effects. Journal of the American Statistical Association, 112 (50). 1720 - 1732. ISSN 0162-1459

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Abstract

Abadie and Imbens (2008) showed 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 by Abadie and Imbens (2006). As an empirical illustration, we apply the proposed method to the National Supported Work data.

Item Type:	Article
Official URL:	http://www.tandfonline.com/toc/UASA20/current
Additional Information:	© 2017 Taylor & Francis
Divisions:	Economics
Subjects:	Q Science > QA Mathematics
Date Deposited:	05 Sep 2017 13:25
Last Modified:	26 Sep 2024 16:45
Projects:	SES-0720961
Funders:	European Research Council
URI:	http://eprints.lse.ac.uk/id/eprint/84170

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