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A Simple and Successsful Shrinkage Method for Weighting Estimators of Treatment Effects

Author

Listed:
  • Pohlmeier, Winfried

    (University of Konstanz)

  • Seiberlich, Ruben

    (University of Konstanz)

  • Uysal, Selver Derya

    (Institute for Advanced Studies, Vienna)

Abstract

A simple shrinkage method is proposed to improve the performance of weighting estimators of the average treatment effect. As the weights in these estimators can become arbitrarily large for the propensity scores close to the boundaries, three different variants of a shrinkage method for the propensity scores are analyzed. The results of a comprehensive Monte Carlo study demonstrate that this simple method substantially reduces the mean squared error of the estimators in finite samples, and is superior to several popular trimming approaches over a wide range of settings.

Suggested Citation

  • Pohlmeier, Winfried & Seiberlich, Ruben & Uysal, Selver Derya, 2014. "A Simple and Successsful Shrinkage Method for Weighting Estimators of Treatment Effects," Economics Series 304, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:304
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    File URL: https://irihs.ihs.ac.at/id/eprint/2891
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    References listed on IDEAS

    as
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    Cited by:

    1. Heiler, Phillip & Kazak, Ekaterina, 2021. "Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores," Journal of Econometrics, Elsevier, vol. 222(2), pages 1083-1108.
    2. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.

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    More about this item

    Keywords

    Average treatment effect; Econometric evaluation; Penalizing; Propensity score; Shrinkage;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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