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

Mingfeng Zhan, Zongwu Cai, Ying Fang and Ming Lin
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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

No 202020, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

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.

Keywords: Covariate balance; Propensity score; Treatment effects (search for similar items in EconPapers)
JEL-codes: C3 C5 (search for similar items in EconPapers)
Date: 2020-12, Revised 2020-12
New Economics Papers: this item is included in nep-ecm
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