期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 112, 期 520, 页码 1720-1732出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2016.1231613
关键词
Bootstrap; Matching; Treatment effect
资金
- ERC Consolidator Grant [SES-0720961]
- Direct For Social, Behav & Economic Scie
- Divn Of Social and Economic Sciences [1623684] Funding Source: National Science Foundation
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 theweighted bootstrap. The key is to construct bootstrap counterparts by resampling based on certain linear forms of the estimators. Ourweighted 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 bootstrapmethod 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.
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