Robust Post-Matching Inference
Robust Post-Matching Inference
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DOI：
10.1080/01621459.2020.1840383
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发表时间：
2021-01-11
影响因子：
3.7
通讯作者：
Spiess, Jann
中科院分区：
文献类型：
--
作者：
Abadie, Alberto;Spiess, Jann
在观察性研究中，最近邻匹配是一种流行的非参数工具，用于在实验组和对照组之间建立平衡。作为预处理步骤在回归之前,匹配减少依赖参数的建模假设。然而，在目前的经验实践中，在标准误差和置信区间的计算中，往往忽略了匹配步骤。在本文中，我们表明，如果不进行替换而进行匹配，并且回归模型相对于结果变量对处理变量和用于匹配的所有协变量的总体回归函数是正确指定的，则忽略匹配步骤会导致渐近有效的标准误差。但是，如果用替换进行匹配，或者更重要的是，如果在上述意义上错误地指定第二步回归模型，则忽略匹配步骤的标准误差是无效的。此外，回归模型的正确规范不需要与匹配数据一致地估计治疗效果。我们证明了两个容易实现的替代方案产生了对后匹配估计量分布的近似，这些近似对错误规范具有鲁棒性。一个模拟研究和一个实证例子证明了我们的结果的实证相关性。这篇文章可以在网上找到。
Nearest-neighbor matching is a popular nonparametric tool to create balance between treatment and control groups in observational studies. As a preprocessing step before regression, matching reduces the dependence on parametric modeling assumptions. In current empirical practice, however, the matching step is often ignored in the calculation of standard errors and confidence intervals. In this article, we show that ignoring the matching step results in asymptotically valid standard errors if matching is done without replacement and the regression model is correctly specified relative to the population regression function of the outcome variable on the treatment variable and all the covariates used for matching. However, standard errors that ignore the matching step are not valid if matching is conducted with replacement or, more crucially, if the second step regression model is misspecified in the sense indicated above. Moreover, correct specification of the regression model is not required for consistent estimation of treatment effects with matched data. We show that two easily implementable alternatives produce approximations to the distribution of the post-matching estimator that are robust to misspecification. A simulation study and an empirical example demonstrate the empirical relevance of our results. for this article are available online.