Abstract
Different covariate balance weighting methods have been proposed by researchers from different perspectives to estimate the treatment effects. This paper gives a brief review of the covariate balancing propensity score method by Imai and Ratkovic (2014), the stable balance weighting procedure by Zubizarreta (2015), the calibration balance weighting approach by Chan, et al. (2016), and the integrated propensity score technique by Sant’Anna, et al. (2020). Simulations are conducted to illustrate the finite sample performance of both the average treatment effect and quantile treatment effect estimators based on different weighting methods. Simulation results show that in general, the covariate balance weighting methods can outperform the conventional maximum likelihood estimation method while the performance of the four covariate balance weighting methods varies with the data generating processes. Finally, the four covariate balance weighting methods are applied to estimate the treatment effects of the college graduate on personal annual income.
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Acknowledgements
The authors thank Professor José R. Zubizarreta for providing the sbw R package, Professor Pedro H. C. Sant’Anna and Xu Qi for their kind help with the IPS R package, and Dr. Noah Greifer for his helpful discussion.
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This research was supported by the National Natural Science Foundation of China under Grant Nos. 71631004 and 72033008, the National Science Foundation for Distinguished Young Scholars under Grant No. 71625001, and the Science Foundation of Ministry of Education of China under Grant No. 19YJA910003.
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Zhan, M., Fang, Y. & Lin, M. Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects. J Syst Sci Complex 35, 2263–2277 (2022). https://doi.org/10.1007/s11424-022-1037-4
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DOI: https://doi.org/10.1007/s11424-022-1037-4