Outliers in Semi-Parametric Estimation of Treatment Effects
Outliers in Semi-Parametric Estimation of Treatment Effects
复制标题
治疗效果半参数估计中的异常值
DOI：
10.2139/ssrn.3063316
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Darwin Ugarte Ontiveros
中科院分区：
文献类型：
--
作者：
G. Canavire;Luis Castro Peñarrieta;Darwin Ugarte Ontiveros
异常值可能特别难以检测，从而在半参数估计中产生偏差和不一致。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，在异常值的存在下是有偏差的。坏的和好的杠杆点异常值被考虑。在不良杠杆点的情况下会产生偏差，因为它们完全改变了用于定义反事实的度量的分布；另一方面，好的杠杆点增加了打破共同支持条件的机会，并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得分或距离度量。我们根据规模和位置的Stahel-Donoho （SD）多元估计量和多元位置的s -估计量（Smultiv）的精神，提供了一些识别和纠正异常值影响的线索。并将此策略应用于实验数据。
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.
影响因子：
7.2
作者：
Stuart, Elizabeth A.;Lee, Brian K.;Leacy, Finbarr P.
通讯作者：
Leacy, Finbarr P.