  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.
