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Existing propensity score methods rely on a correctly specified propensity score model. Model misspecification results in biased treatment effect estimation and covariate imbalance. We proposed a method for the propensity score analysis with controlled subgroup balance (G-SBPS) to achieve covariate mean balance in all subgroups. We further incorporated nonparametric kernel regression for the propensity scores and developed a kernelized G-SBPS (kG-SBPS) to improve the subgroup mean balance of covariate transformations in a rich functional class. This extension increased robustness to propensity score model misspecification. Extensive numerical studies showed that G-SBPS and kG-SBPS improve both subgroup covariate balance and subgroup treatment effect estimation, compared to existing approaches. 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Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge, MA, USA: The MIT Press, 2002."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1515\/jci-2015-0026"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201800132"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1515\/ijb-2012-0030"},{"key":"e_1_3_2_26_2","first-page":"1155","article-title":"Kernel balancing: a flexible non-parametric weighting procedure for estimating causal effects","volume":"30","author":"Hazlett C","year":"2020","unstructured":"Hazlett C. Kernel balancing: a flexible non-parametric weighting procedure for estimating causal effects. 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