Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects

doi:10.1007/s11424-022-1037-4

Journal of Systems Science and Complexity ›› 2022, Vol. 35 ›› Issue (6): 2263-2277.DOI: 10.1007/s11424-022-1037-4

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Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects

ZHAN Mingfeng¹, FANG Ying^1,2, LIN Ming^1,2

1. Wang Yanan Institute for Studies in Economics and Fujian Key Laboratory of Statistical Sciences, Xiamen University, Xiamen 361005, China;
2. Department of Statistics and Data Science, Xiamen University, Xiamen 361005, China

Received:2021-02-21 Revised:2021-08-05 Online:2022-11-25 Published:2022-12-23
Contact: LIN Ming,Email:linming50@xmu.edu.cn
Supported by:
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 Mingfeng, FANG Ying, LIN Ming. Comparison of Covariate Balance Weighting Methods in Estimating Treatment Effects[J]. Journal of Systems Science and Complexity, 2022, 35(6): 2263-2277.

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.

Key words: Average treatment effect, calibration balance weighting, covariate balance, integrated propensity score, quantile treatment effects, stable balance weighting

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