{"title":"Double machine learning for partially linear mediation models with high-dimensional confounders","authors":"Jichen Yang, Yujing Shao, Jin Liu, Lei Wang","doi":"10.1016/j.neucom.2024.128766","DOIUrl":null,"url":null,"abstract":"<div><div>To estimate and statistically infer the direct and indirect effects of exposure and mediator variables while accounting for high-dimensional confounding variables, we propose a partially linear mediation model to incorporate a flexible mechanism of confounders. To obtain asymptotically efficient estimators for the effects of interest under the influence of the nuisance functions with high-dimensional confounders, we construct two Neyman-orthogonal score functions to remove regularization bias. Flexible machine learning methods and data splitting with cross-fitting are employed to address the overfitting issue and estimate unknown nuisance functions efficiently. We rigorously investigate the asymptotic expressions of the proposed estimators for the direct, indirect and total effects and then derive their asymptotic normality properties. In addition, two Wald statistics are constructed to test the direct and indirect effects, respectively, and their limiting distributions are obtained. The satisfactory performance of our proposed estimators is demonstrated by simulation results and a genome-wide analysis of blood DNA methylation dataset.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"614 ","pages":"Article 128766"},"PeriodicalIF":5.5000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231224015376","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
To estimate and statistically infer the direct and indirect effects of exposure and mediator variables while accounting for high-dimensional confounding variables, we propose a partially linear mediation model to incorporate a flexible mechanism of confounders. To obtain asymptotically efficient estimators for the effects of interest under the influence of the nuisance functions with high-dimensional confounders, we construct two Neyman-orthogonal score functions to remove regularization bias. Flexible machine learning methods and data splitting with cross-fitting are employed to address the overfitting issue and estimate unknown nuisance functions efficiently. We rigorously investigate the asymptotic expressions of the proposed estimators for the direct, indirect and total effects and then derive their asymptotic normality properties. In addition, two Wald statistics are constructed to test the direct and indirect effects, respectively, and their limiting distributions are obtained. The satisfactory performance of our proposed estimators is demonstrated by simulation results and a genome-wide analysis of blood DNA methylation dataset.
为了估计和统计推断暴露变量和中介变量的直接和间接效应,同时考虑高维混杂变量,我们提出了一个部分线性中介模型,以纳入灵活的混杂因素机制。为了在高维混杂变量滋扰函数的影响下获得渐进有效的相关效应估计值,我们构建了两个奈曼正交得分函数来消除正则化偏差。我们采用灵活的机器学习方法和交叉拟合的数据拆分来解决过拟合问题,并高效地估计未知的滋扰函数。我们严格研究了所提出的直接效应、间接效应和总效应估计器的渐近表达式,然后推导出它们的渐近正态性质。此外,我们还构建了两个 Wald 统计量,分别用于检验直接效应和间接效应,并得到了它们的极限分布。模拟结果和对血液 DNA 甲基化数据集的全基因组分析表明,我们提出的估计值具有令人满意的性能。
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.