When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of covariates unrelated to the outcome in the propensity score model led to bias and large variance in the estimator of treatment effects. Many data-driven covariate selection methods have been proposed for selecting covariates related to outcomes. However, most of them assume an average treatment effect estimation and may not be designed to estimate quantile treatment effects (QTE), which is the effect of treatment on the quantiles of outcome distribution. In QTE estimation, we consider two relation types with the outcome as the expected value and quantile point. To achieve this, we propose a data-driven covariate selection method for propensity score models that allows for the selection of covariates related to the expected value and quantile of the outcome for QTE estimation. Assuming the quantile regression model as an outcome regression model, covariate selection was performed using a regularization method with the partial regression coefficients of the quantile regression model as weights. The proposed method was applied to artificial data and a dataset of mothers and children born in King County, Washington, to compare the performance of existing methods and QTE estimators. As a result, the proposed method performs well in the presence of covariates related to both the expected value and quantile of the outcome.

翻译：在使用倾向得分方法估计处理效应时，选择纳入倾向得分模型的协变量至关重要。在倾向得分模型中纳入与结果无关的协变量会导致处理效应估计出现偏差和较大方差。目前已有多种数据驱动的协变量选择方法被提出用于筛选与结果相关的协变量。然而，这些方法大多假设估计平均处理效应，可能不适用于估计分位数处理效应（QTE），即处理对结果分布分位数的影响。在QTE估计中，我们需考虑结果与期望值及分位点两类关系。为此，我们提出一种数据驱动的倾向得分模型协变量选择方法，能够针对QTE估计筛选与结果期望值和分位数相关的协变量。以分位数回归模型作为结果回归模型，利用该模型偏回归系数作为权重的正则化方法实现协变量选择。将所提方法应用于人工数据集及华盛顿州金县母婴出生数据集，并与现有方法及QTE估计器进行性能比较。结果表明，当存在同时与结果期望值和分位数相关的协变量时，所提方法表现良好。