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英文文献：Outliers in semi-parametric Estimation of Treatment Effects-处理效果半参数估计中的异常值
英文文献作者：Darwin Ugarte Ontiveros,Gustavo Canavire-Bacarreza,Luis Castro Pe?arrieta
英文文献摘要：
Average treatment effects estimands can present significant bias under the presence of outliers. Moreover, outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric ATE estimads. In this paper, we use Monte Carlo simulations to demonstrate that semi-parametric methods, such as matching, are biased in the presence of outliers. Bad and good leverage points outliers are considered. The bias arises because bad leverage points completely change the distribution of the metrics used to define counterfactuals. Whereas good leverage points increase the chance of breaking the common support condition and distort the balance of the covariates and which may push practitioners to misspecify the propensity score. We provide some clues to diagnose the presence of outliers and propose a reweighting estimator that is robust against outliers based on the Stahel-Donoho multivariate estimator of scale and location. An application of this estimator to LaLonde’s (1986) data allows us to explain the Dehejia and Wahba (2002) and Smith and Todd (2005) debate on the inability of matching estimators to deal with the evaluation problem.
在异常值存在的情况下，平均处理效果评价可能呈现显著的偏倚。此外，异常值特别难以检测，在半参数的ATE估计中造成偏差和不一致。在本文中，我们使用蒙特卡罗模拟来证明半参数方法，如匹配，是有偏差的存在。坏的和好的杠杆点被考虑到异常值。产生偏差是因为不良的杠杆点完全改变了用于定义反事实的度量标准的分布。而好的杠杆点增加了打破共同支持条件的机会，扭曲了协变量的平衡，这可能会促使从业者错误地指定倾向性评分。我们提供了一些线索来诊断异常值的存在，并提出了一个基于staher - donoho多变量尺度和位置估计器对异常值稳健的重加权估计器。将该估计量应用于LaLonde(1986)数据，可以解释Dehejia和Wahba(2002)以及Smith和Todd(2005)关于匹配估计量无法处理评估问题的争论。
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