Outliers in semi-parametric estimation of treatment effects

Artikel

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 9 ; Year: 2021 ; Issue: 2 ; Pages: 1-32 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Semiparametric and Nonparametric Methods: General ; Model Evaluation, Validation, and Selection ; Estimation: General
• Thema: mahalanobis distance ; outliers ; propensity score ; treatment effects

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Canavire-Bacarreza, Gustavo ; Castro, Luis ; Ontiveros, Darwin Ugarte
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2021

Weitere Informationen

• DOI: doi:10.3390/econometrics9020019
• Handle: http://hdl.handle.net/10419/247609
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ