期刊
STATISTICS & PROBABILITY LETTERS
卷 204, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.spl.2023.109933
关键词
Average causal effect; Censored data; Propensity score; Data -rich environment
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.
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.
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