Quantile outcome adaptive lasso: Covariate selection for inverse probability weighting estimator of quantile treatment effects.

ABSTRACT: 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 (QTEs), which are the effects 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

SUBMITTER: Shoji T 

PROVIDER: S-EPMC11800702 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature