用深度学习提升因果中介分析精度，无需假设异质性。
Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity
- 先用TARNet学习共享协变量表示，再结合G估计
- 相比传统方法，中介系数标准误降低1.45至1.51倍
- 适合处理非正态协变量和非线性效应的因果研究
我们提出一种两阶段估计器UNIT，用于在无本质异质性（NEH）假设下估计结构中介参数。第一阶段通过TARNet学习随机处理对中介变量的异质性效应，基于跨处理组的共享协变量表示。得到的条件平均处理效应（CATE）估计值作为插值权重，用于修正郑和周（2015）的G估计方程中权重函数的异质性部分，即使存在未观测的中介-结果混杂也能识别结构参数。仿真显示，在非高斯协变量和非线性中介效应条件下，当样本量n≥2000时，使用TARNet权重可使第二阶段中介系数的标准误下降1.45至1.51倍，且不增加偏差或降低覆盖率。
原文摘要 · Abstract (English)
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.
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