发表机构
Methods Center University of Tübingen（图宾根大学方法中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对半参数因果中介分析提出UNIT方法，通过两阶段估计，第一阶段用TARNet学习共享协变量表示估计异质效应，为权重函数提供插件近似以识别结构参数，模拟显示其能有效降低标准误差，提升估计精度。
AI 中文摘要
我们提出了一种用于结构中介参数的两阶段估计器，它在“无本质异质性”（NEH）假设下将深度表示学习与G估计相结合。我们将该方法称为UNIT。在第一阶段，TARNet通过学习跨处理的共享协变量表示来估计随机处理对中介变量的异质效应，由此得到的条件平均处理效应（CATE）估计为进入Zheng和Zhou（2015）的G估计方程的权重函数的异质性相关成分提供了插件近似，即使存在未测量的中介变量 - 结果混杂也能识别结构参数。我们表明更准确的第一阶段表示学习可以产生更有信息的插件权重，从而提高结构参数估计器的精度。在具有非高斯协变量和非线性中介效应的模拟中，相对于经典方法，TARNet权重将中介系数的第二阶段标准误差降低了1.45至1.51倍（重复实验的中位数，n≥2000），且不影响偏差或覆盖率。
英文摘要
We propose a two-stage estimator for structural mediation parameters that combines deep representation learning with G-estimation under the "no essential heterogeneity" (NEH) assumption. We call the method UNIT. In the first stage,TARNet estimates the heterogeneous effect of a randomized treatment on a mediator by learning a shared covariate representation across treatment arms.The resulting conditional average treatment effect (CATE) estimate provides a plug-in approximation to the heterogeneity-dependent component of the weight function entering the G-estimating equation of Zheng and Zhou (2015), which identifies the structural parameters even in the presence of unmeasured mediator-outcome confounding. We show that more accurate first-stage representation learning can yield a more informative plug-in weight and thereby improve the precision of the structural parameter estimator. In simulations with non-Gaussian covariates and nonlinear mediator effects, TARNet weights reduce the Stage-2 standard error of the mediation coefficient by a factor of $1.45$ to $1.51$ (median across replications, $n \ge 2000$) relative to the classical approach, at no cost to bias or coverage.
Comments30 pages, 2 figures
