This paper proposes a new non-parametric bootstrap method to quantify the uncertainty of average treatment effect estimate for the treated from matching estimators. More specifically, it seeks to quantify the uncertainty associated with the average treatment effect estimate for the treated by bootstrapping the treatment group only and finding the counterpart control group by pair matching on estimated propensity score without replacement. We demonstrate the validity of this approach and compare it with existing bootstrap approaches through Monte Carlo simulation and analysis of a real world data set. The results indicate that the proposed approach constructs confidence intervals and standard errors that have 95 percent or above coverage rate and better precision compared with existing bootstrap approaches, while these measures also depend on percent treated in the sample data and the sample size.

翻译：本文提出了一种新的非参数自助法，用于量化基于匹配估计量所得处理组平均处理效应的不确定性。具体而言，该方法旨在通过仅对处理组进行自助抽样，并基于无放回的倾向得分匹配为每个自助样本寻找对应的控制组，从而量化处理组平均处理效应估计的相关不确定性。我们通过蒙特卡洛模拟和真实数据集分析，验证了该方法的有效性，并将其与现有的自助法进行了比较。结果表明，与现有自助法相比，所提方法构建的置信区间和标准误具有95%或以上的覆盖率及更优的精度，同时这些度量指标也取决于样本数据中处理组的百分比以及样本量。