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The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

Author

Listed:
  • Bodory, Hugo

    (University of St. Gallen)

  • Camponovo, Lorenzo

    (University of St. Gallen)

  • Huber, Martin

    (University of Fribourg)

  • Lechner, Michael

    (University of St. Gallen)

Abstract

This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor market data from Germany and varies w.r.t. treatment selectivity, effect heterogeneity, the share of treated, and the sample size. The results suggest that in general, the bootstrap procedures dominate the asymptotic ones in terms of size and power for both matching and weighting estimators. Furthermore, the results are qualitatively quite robust across the various simulation features.

Suggested Citation

  • Bodory, Hugo & Camponovo, Lorenzo & Huber, Martin & Lechner, Michael, 2016. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," IZA Discussion Papers 9706, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp9706
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    References listed on IDEAS

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    More about this item

    Keywords

    matching; treatment effects; variance estimation; inference; inverse probability weighting;
    All these keywords.

    JEL classification:

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

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