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Covariate Balance Weighting Methods in Estimating Treatment Effects: An Empirical Comparison

Author

Listed:
  • Mingfeng Zhan

    (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China)

  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

  • Ying Fang

    (The Wang Yanan Institute for Studies in Economics and Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian 361005, China)

  • Ming Lin

    (The Wang Yanan Institute for Studies in Economics and Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian 361005, China)

Abstract

We conduct a series of simulations to compare the finite sample performance of the average treatment effect estimators based on four recently proposed methodologies — the covariate balancing propensity score method, the stable balance weighting approach, the calibration balance weighting procedure, and the integrated propensity score method. Simulation results show that the performance of the four covariate balance weighting methods are generally better than that for the conventional method, maximum likelihood estimation method without covariate balance, and among the four covariate balance weighting methods, it is difficult to tell which covariate balance weighting method can dominate the others.

Suggested Citation

  • Mingfeng Zhan & Zongwu Cai & Ying Fang & Ming Lin, 2020. "Covariate Balance Weighting Methods in Estimating Treatment Effects: An Empirical Comparison," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202020, University of Kansas, Department of Economics, revised Dec 2020.
    • Handle: RePEc:kan:wpaper:202020
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    File URL: http://www2.ku.edu/~kuwpaper/2020Papers/202020.pdf
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    References listed on IDEAS

    as
    1. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    2. Kwun Chuen Gary Chan & Sheung Chi Phillip Yam & Zheng Zhang, 2016. "Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 673-700, June.
    3. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    4. Wan, Shui-Ki & Xie, Yimeng & Hsiao, Cheng, 2018. "Panel data approach vs synthetic control method," Economics Letters, Elsevier, vol. 164(C), pages 121-123.
    5. José R. Zubizarreta, 2015. "Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 910-922, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Covariate balance; Propensity score; Treatment effects;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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