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Outliers in Semi-Parametric Estimation of Treatment Effects

Author

Listed:
  • Gustavo Canavire-Bacarreza

    (Centro de Investigaciones Económicas y Empresariales (CIEE), Universidad Privada Boliviana, La Paz, Bolivia)

  • Luis Castro Peñarrieta

    (Centro de Investigaciones Económicas y Empresariales (CIEE), Universidad Privada Boliviana, La Paz, Bolivia
    División de Economía, Centro de Investigación y Docencia Económicas, A.C. (CIDE), Aguascalientes CP20313, Mexico)

  • Darwin Ugarte Ontiveros

    (Centro de Investigaciones Económicas y Empresariales (CIEE), Universidad Privada Boliviana, La Paz, Bolivia
    Banco Central de Bolivia (BCB), La Paz, Bolivia)

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.

Suggested Citation

  • Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    • Handle: RePEc:gam:jecnmx:v:9:y:2021:i:2:p:19-:d:546793
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    References listed on IDEAS

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    Cited by:

    1. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.

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    More about this item

    Keywords

    treatment effects; outliers; propensity score; mahalanobis distance;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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