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Causal effect estimation with censored outcome and covariate selection

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  • Li, Li
  • Shi, Pengfei
  • Fan, Qingliang
  • Zhong, Wei

Abstract

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.

Suggested Citation

  • Li, Li & Shi, Pengfei & Fan, Qingliang & Zhong, Wei, 2024. "Causal effect estimation with censored outcome and covariate selection," Statistics & Probability Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:stapro:v:204:y:2024:i:c:s0167715223001578
    DOI: 10.1016/j.spl.2023.109933
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    References listed on IDEAS

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