Authors
Yajing Zhou, Kecheng Wei, Yahang Liu, Zhaoyang Li, Chen Huang, Guoyou Qin, Yongfu Yu
Published in
PLoS computational biology. Volume 22. Issue 6. Pages e1014436. Jun 24, 2026. Epub Jun 24, 2026.
Abstract
Substantial progress has been made in the area of causal inference utilizing large-scale data, among which the estimation of causal mediation effects has attracted a lot of attention. However, existing large-dimensional causal inference primarily focuses on total effects or typical causal mediation effects under binary variable settings, placing less emphasis on large-scale covariate selection with continuous treatment and mediator. To address this, we propose a weighted semiparametric estimation framework that integrates the generalized outcome-adaptive LASSO method into generalized propensity score modeling to achieve estimation of causal mediation effects under continuous variable settings. Simulation results show that our proposed method outperforms other regularization-based methods in selection accuracy and estimation efficiency, which is achieved by incorporating outcome-related key variables and excluding noise covariates. From the perspective of achieving a stable balance between efficiency and bias, as well as high-dimensional information filtering, our method may serve as a compelling alternative that balances estimation efficiency with model interpretability and inferential robustness. We further conduct a real-world application based on the UK Biobank database, quantifying the causal mediation effects of apolipoprotein B levels within the association between potential diabetes risk and cancer incidence using large-scale healthcare and medical data.
PMID:
42341064
Bibliographic data and abstract were imported from PubMed on 25 Jun 2026.
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