Estimating the causal treatment effects by subgroups is important in observational studies when the treatment effect heterogeneity may be present. 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 new algorithm, the propensity score analysis with guaranteed 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 is more robust 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. We applied G-SBPS and kG-SBPS to a dataset on right heart catheterization to estimate the subgroup average treatment effects on the hospital length of stay and a dataset on diabetes self-management training to estimate the subgroup average treatment effects for the treated on the hospitalization rate.

翻译：在观察性研究中，当处理效应异质性可能存在时，按子组估计因果处理效应非常重要。现有的倾向性评分方法依赖于正确指定的倾向性评分模型。模型错误设定会导致处理效应估计有偏及协变量不平衡。我们提出了一种新算法——保证子组平衡的倾向性评分分析（G-SBPS），以在所有子组中实现协变量均值平衡。我们进一步将非参数核回归融入倾向性评分，开发了核化G-SBPS（kG-SBPS），以在丰富的函数类中改善协变量变换的子组均值平衡。该扩展对倾向性评分模型错误设定更加稳健。大量数值研究表明，与现有方法相比，G-SBPS和kG-SBPS同时改善了子组协变量平衡和子组处理效应估计。我们将G-SBPS和kG-SBPS应用于右心导管插入术数据集以估计子组平均处理效应（对住院时长的影响），以及糖尿病自我管理培训数据集以估计处理组的子组平均处理效应（对住院率的影响）。