Quantile regression outcome-adaptive lasso: variable selection for causal quantile treatment effect estimation
分位数回归结局自适应 lasso:因果分位数处理效应的变量选择
Quantile treatment effects (QTEs) can characterize the potentially heterogeneous causal effect of a treatment on different points of the entire outcome distribution. Propensity score (PS) methods are commonly employed for estimating QTEs in non-randomized studies. Empirical and theoretical studies have shown that insufficient and unnecessary adjustment for covariates in PS models can lead to bias and efficiency loss in estimating treatment effects. Striking a balance between…