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How to control for many covariates? Reliable estimators based on the propensity score

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  • Martin Huber

    ()

  • Michael Lechner

    ()

  • Conny Wunsch

    ()

Abstract

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observable covariates is required, like inverse pro¬bability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design that are of practical importance, like sample size, the type of the outcome variable, and aspects of the selection process. We find that trimming individual observations with too much weight as well as the choice of tuning parameters is important for all estimators. The key conclusion from our simulations is that a particular radius matching estimator combined with regression performs best overall, in particular when robustness to misspecifications of the propensity score is considered an important property.

Suggested Citation

  • Martin Huber & Michael Lechner & Conny Wunsch, 2010. "How to control for many covariates? Reliable estimators based on the propensity score," University of St. Gallen Department of Economics working paper series 2010 2010-30, Department of Economics, University of St. Gallen.
  • Handle: RePEc:usg:dp2010:2010-30
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    Cited by:

    1. Johannes Van Biesebroeck & Emily Yu & Shenjie Chen, 2015. "The impact of trade promotion services on Canadian exporter performance," Canadian Journal of Economics, Canadian Economics Association, vol. 48(4), pages 1481-1512, November.
    2. Dolton, Peter & Smith, Jeffrey A., 2011. "The Impact of the UK New Deal for Lone Parents on Benefit Receipt," IZA Discussion Papers 5491, Institute for the Study of Labor (IZA).
    3. Rotger, Gabriel Pons & Gørtz, Mette & Storey, David J., 2012. "Assessing the effectiveness of guided preparation for new venture creation and performance: Theory and practice," Journal of Business Venturing, Elsevier, vol. 27(4), pages 506-521.
    4. Felfe, Christina & Lechner, Michael & Steinmayr, Andreas, 2011. "Sports and Child Development," CEPR Discussion Papers 8523, C.E.P.R. Discussion Papers.
    5. Mario Liebensteiner, 2014. "Estimating the Income Gain of Seasonal Labor Migration," Review of Development Economics, Wiley Blackwell, vol. 18(4), pages 667-680, November.
    6. Martin Biewen & Bernd Fitzenberger & Aderonke Osikominu & Marie Paul, 2014. "The Effectiveness of Public-Sponsored Training Revisited: The Importance of Data and Methodological Choices," Journal of Labor Economics, University of Chicago Press, vol. 32(4), pages 837-897.
    7. Karolina Goraus & Joanna Tyrowicz, 2013. "The Goodwill Effect? Female Access to the Labor Market Over Transition: A Multicountry Analysis," Working Papers 2013-19, Faculty of Economic Sciences, University of Warsaw.
    8. Millimet, Daniel L. & Roy, Jayjit, 2011. "Three New Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous," IZA Discussion Papers 5911, Institute for the Study of Labor (IZA).
    9. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    10. Ferraro, Paul J. & Miranda, Juan José, 2014. "The performance of non-experimental designs in the evaluation of environmental programs: A design-replication study using a large-scale randomized experiment as a benchmark," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 344-365.
    11. Paweł Strawiński, 2012. "Small sample properties of matching with caliper," Working Papers 2012-13, Faculty of Economic Sciences, University of Warsaw.
    12. Paweł Strawiński, 2013. "Controlling for overlap in matching," Working Papers 2013-10, Faculty of Economic Sciences, University of Warsaw.
    13. Huber, Martin, 2012. "Identifying causal mechanisms in experiments (primarily) based on inverse probability weighting," Economics Working Paper Series 1213, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    14. repec:oup:scippl:v:44:y:2017:i:4:p:497-512. is not listed on IDEAS
    15. Caliendo, Marco & Künn, Steffen & Schmidl, Ricarda, 2011. "Fighting Youth Unemployment: The Effects of Active Labor Market Policies," IZA Discussion Papers 6222, Institute for the Study of Labor (IZA).
    16. Göbel, Christian & Zwick, Thomas, 2013. "Are personnel measures effective in increasing productivity of old workers?," Labour Economics, Elsevier, vol. 22(C), pages 80-93.
    17. Bühler, Stefan & Helm, Marco & Lechner, Michael, 2011. "Trade Liberalization and Growth: Plant-Level Evidence from Switzerland," Economics Working Paper Series 1133, University of St. Gallen, School of Economics and Political Science.

    More about this item

    Keywords

    Propensity score matching; kernel matching; inverse probability weighting; selection on observables; empirical Monte Carlo study; finite sample properties;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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