Semiparametric Causal Mediation Analysis for Linear Models with Non-Gaussian Errors: Applications to Drug Treatment and Social Program Evaluation
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非高斯误差线性模型的半参数因果中介分析：药物治疗与社会项目评估
- 研究问题
- 在线性中介模型存在非高斯误差及处理与中介交互时，如何更精确地估计直接和间接效应，并构造可靠区间？
- 方法与思路
- 作者结合半参数有效回归、多起点拟合算法与堆叠估计方程，处理标准及含处理与中介交互的模型，构造置信区间。
- 材料陈述的贡献
- 摘要称，模拟中该方法相对 OLS 降低均方根误差并缩短区间，在药物治疗与社会项目数据中提供更精确的效应分解。
- 阅读关注点
- 应核对因果识别条件、误差分布允许范围、有效性证明及多起点算法，并检查边界功效设计与区间覆盖率。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明完整识别条件，特定模拟优势不能直接推广。
原文摘要
\textbf{Background:} Mediation analysis is widely used to investigate how treatments and programs exert their effects, but standard ordinary least squares (OLS) inference can be unreliable when regression errors are non-Gaussian. In medical and public-health studies, this can affect whether indirect and direct effects are judged clinically or scientifically meaningful. \textbf{Methods:} We developed a semiparametric causal mediation framework for linear models allowing possibly non-Gaussian errors, covering both standard models and models with treatment--mediator interaction. The method combines semiparametric efficient regression estimation, a reproducible multi-start fitting algorithm for numerical stability, and stacked estimating equations for confidence-interval construction without requiring Gaussian error assumptions. \textbf{Results:} Across Gaussian, skewed, and mixture-error simulations, the semiparametric estimator reduced root mean squared error and confidence-interval length relative to OLS, with the largest gains under non-Gaussian errors. In a near-boundary power design, the OLS confidence interval achieved 18.3\% empirical power, whereas the semiparametric confidence interval identified significant effects in all replications. In the \textit{uis} drug-treatment data, it yielded sharper treatment-specific effect estimates under clear treatment--mediator interaction. In the \textit{jobs} social-program data, the semiparametric analysis produced shorter confidence intervals for mediated effects and detected nonzero mediation where OLS did not. \textbf{Conclusions:} Semiparametric mediation analysis can improve the precision and reliability of effect decomposition in studies with non-Gaussian outcomes, offering a practical alternative to OLS when indirect and direct effects may inform clinical or policy decision-making.