Causal effect estimation with censored outcome and covariate selection

We estimate the causal effect in the presence of censored outcome and high-dimensional covariates. To improve the efficiency of the estimation of average causal effect, we propose the censored outcome adaptive Lasso (COAL) to select covariates.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

We investigate the estimation of causal effect in the presence of censored outcome and high-dimensional covariates. To enhance the efficiency of average causal effect estimation, we propose the censored outcome adaptive Lasso (COAL) for covariate selection.