Outliers in Semi-Parametric Estimation of Treatment Effects

Gustavo Canavire-Bacarreza, Luis Castro Peñarrieta and Darwin Ugarte Ontiveros ()

Econometrics, 2021, vol. 9, issue 2, 1-32

Abstract: 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.

Keywords: treatment effects; outliers; propensity score; mahalanobis distance (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Outliers in semi-parametric Estimation of Treatment Effects (2017)
Working Paper: Outliers in semi-parametric Estimation of Treatment Effects (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:9:y:2021:i:2:p:19-:d:546793

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