发表机构
Yale School of Public Health（耶鲁大学公共卫生学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出正交机器学习框架估计条件异质性中介效应，通过加权Neyman正交损失和两阶段元学习实现，模拟中误差降低超50%，并在三个数据集上揭示异质性效应。
AI 中文摘要
因果中介分析将干预对结局的总效应分解为直接路径和通过中介变量传递的间接路径，但标准方法通常使用总体平均效应来概括这些路径。然而，在许多应用中，间接效应可能因个体特征的不同而存在显著差异。我们提出了一种正交统计学习框架，用于估计以个体特征为条件的异质性因果中介效应。该方法基于加权总体平均效应的影响函数表示，构建了一类加权Neyman正交损失函数。这些损失直接针对条件中介估计量，其最小化器对干扰估计误差局部不敏感。我们在两阶段元学习框架下实现所提出的学习器，使用正则化线性筛作为第二阶段平滑器，并引入目标学习与正交学习的组合，以在介体密度比不稳定时提高稳定性。我们建立了L2和一致极限理论，并开发了点态和一致置信带。模拟研究表明，与现有基于模型的方法相比，所提出的正交学习器将平均积分平方误差降低了超过50%，并在非线性设置中提供了计算高效的推断。CARDIA、PSACR和STAR分析揭示了心脏代谢、心理和教育背景下的异质性中介效应。
英文摘要
Causal mediation analysis decomposes the total effect of an intervention on an outcome into a direct pathway and an indirect pathway transmitted through a mediator, but standard methods typically summarize these pathways using population average effects. In many applications, however, the indirect effect may vary substantially across individual profiles. We propose an orthogonal statistical learning framework for estimating heterogeneous causal mediation effects conditional on individual characteristics. The method constructs a class of weighted Neyman orthogonal losses motivated by influence function representations of weighted population average effects. These losses directly target conditional mediation estimands whose minimizers are locally insensitive to nuisance estimation errors. We implement the resulting learners under a two-stage meta-learning framework with regularized linear sieves as second-stage smoothers, and introduce a combination of targeted learning and orthogonal learning designed to improve stability when mediator density ratios are unstable. We establish $L^2$ and uniform limit theory and develop pointwise and uniform confidence bands. Simulation studies show that the proposed orthogonal learners reduce the mean integrated squared error by more than $50\%$ compared with existing model-based methods and provide computationally efficient inference in nonlinear settings. The CARDIA, PSACR, and STAR analyses reveal heterogeneous mediated effects across cardiometabolic, psychological, and educational settings.
