Journal
STATISTICS & PROBABILITY LETTERS
Volume 204, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.spl.2023.109933
Keywords
Average causal effect; Censored data; Propensity score; Data -rich environment
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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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