文章基本信息

标题：Outliers in Semi-Parametric Estimation of Treatment Effects
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作者：Gustavo Canavire-Bacarreza ; Gustavo Canavire-Bacarreza ; Gustavo Canavire-Bacarreza 等
期刊名称：Econometrics
印刷版ISSN：2225-1146
出版年度：2021
卷号：9
期号：1
页码：19
DOI：10.3390/econometrics9020019
语种：English
出版社：MDPI, Open Access Journal
摘要：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.