Outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric estimates. 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 point outliers are considered. Bias arises in the case of bad leverage points because they completely change the distribution of the metrics used to define counterfactuals; good leverage points, on the other hand, increase the chance of breaking the common support condition and distort the balance of the covariates, which may push practitioners to misspecify the propensity score or the distance measures. We provide some clues to identify and correct for the effects of outliers following a reweighting strategy in the spirit of the Stahel-Donoho (SD) multivariate estimator of scale and location, and the S-estimator of multivariate location (Smultiv). An application of this strategy to experimental data is also implemented.

中文翻译：

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半参数估计治疗效果中的异常值

异常值可能特别难以检测，从而在半参数估计中造成偏差和不一致。在本文中，我们使用蒙特卡洛模拟来证明半参数方法（例如匹配）在存在异常值时存在偏差。好的和坏的杠杆率点离群值都在考虑之列。杠杆点差的情况下会产生偏差，因为它们会完全改变用于定义反事实的指标的分布；另一方面，良好的杠杆点会增加打破共同支持条件的机会，并扭曲协变量的平衡，这可能会使从业者错误指定倾向得分或距离度量。我们提供了一些线索，可以根据Stahel-Donoho（SD）规模和位置的多元估计器以及多元位置的S估计器（Smultiv）的精神，通过重加权策略来识别和纠正异常值的影响。还将这种策略应用于实验数据。