Bootstrap Inference of Matching Estimators for Average Treatment Effects

• 成果类型: Article
• 署名作者: Otsu, Taisuke; Rai, Yoshiyasu
• 署名单位: University of London; London School Economics & Political Science; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1231613
• 发表日期: 2017
• 页码: 1720-1732
• 关键词: finite-sample properties; propensity-score; training-programs; Wild Bootstrap; regression; jackknife; causal

摘要

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.