中介分析的能量平衡权重
Energy Balancing Weights for Mediation Analysis
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中文总结 AI 辅助
本研究针对因果中介分析提出EBWMA方法，无需建模处理分配等，通过二次规划最小化能量距离，模拟与实证分析显示其偏差、均方根误差及变异性表现优于对比方法。
中文摘要 AI 辅助
因果中介分析需要重构反事实分布以估计自然直接效应与间接效应。逆概率加权估计量依赖于处理分配和中介密度比的模型，而矩平衡方法要求研究者预先指定应平衡的协变量与中介的哪些函数。我们提出中介分析的能量平衡权重（EBWMA），其针对用于识别反事实均值（如E[Y(1, M(0))]）的联合中介-协变量分布。在自然效应的标准识别条件下，EBWMA构造权重，使加权经验分布逼近该目标，无需对处理分配、中介密度比或结局回归建模。这些权重通过两个顺序求解的二次规划问题最小化能量距离。在具有非线性变换、偏态或二元协变量，以及非线性中介和结局模型的模拟中，EBWMA通常实现了良好的偏差和均方根误差，且蒙特卡洛变异性始终低于基于梯度提升的逆概率加权和矩平衡权重。在对第一次全国健康与营养检查调查流行病学随访研究的说明性分析中，EBWMA对大多数协变量和中介给出了最小的标准化均值差。
英文摘要
Causal mediation analysis requires reconstruction of counterfactual distributions to estimate natural direct and indirect effects. Inverse probability weighting estimators rely on models for treatment assignment and mediator density ratios, whereas moment balancing approaches require researchers to specify in advance which functions of the covariates and mediators should be balanced. We propose Energy Balancing Weights for Mediation Analysis (EBWMA), which targets the joint mediator-covariate distribution used to identify counterfactual means such as E[Y(1, M(0))]. Under standard identification conditions for natural effects, EBWMA constructs weights whose weighted empirical distribution approximates this target, without modeling treatment assignment, mediator density ratios, or the outcome regression. The weights minimize energy distance through two quadratic programming problems solved sequentially. In simulations with nonlinearly transformed, skewed, or binary covariates and nonlinear mediator and outcome models, EBWMA generally achieved favorable bias and root mean squared error, with uniformly lower Monte Carlo variability than gradient boosting-based inverse probability weighting and moment balancing weights. In an illustrative analysis of the National Health and Nutrition Examination Survey I Epidemiologic Follow-up Study, EBWMA gave the smallest standardized mean differences for most covariates and for the mediator.
