AI 中文总结
该研究构建了单一中断时间序列的因果中介分析框架，采用稳定中介加权估计自然直接与间接效应，经模拟和安大略省车辆排放政策案例验证，有效降低了中介-结果混杂下的偏差并提升了效应估计的覆盖率。
AI 中文摘要
针对在固定时间引入并通过单一总体结果序列进行评估的人口层面政策，中断时间序列设计用于估计干预后结果的总变化。当预期政策通过可测量的路径发挥作用时，总效应的信息价值低于其分解为直接效应和间接效应后的信息价值。我们针对单一中断时间序列构建因果中介分析框架，并研究稳定中介加权作为自然直接效应和间接效应的估计量。由于干预是确定性函数，暴露权重等于1，且暴露对比通过分段回归水平变化识别，因此仅对中介路径进行加权。我们添加携带滞后混杂因素历史的累积中介权重，将同期事件作为第二次中断纳入，并将将估计权重视为固定值的方差公式替换为块残差自助法，该方法保持确定性暴露时间不变，以移动块形式重采样中介变量和结果的残差。在针对每日数据校准的模拟中，未加权的系数乘积估计量在中介-结果混杂下存在偏差，间接效应偏差接近0.19，覆盖率为0.003；而稳定加权将偏差降低至约0.03，并将间接效应覆盖率从接近0提升至约0.83。将该方法应用于2019年安大略省Drive Clean车辆排放检测项目的终止，估计得出地面臭氧的直接减少量为2.113ppb（95%置信区间-3.384至-0.841），该结果在多伦多四个区域均表现稳健，通过二氧化氮产生的间接效应为正但存在异质性，总效应接近显著性边界；疫情前的敏感性分析与主要结果一致。
英文摘要
Population-level policies are introduced at a fixed time and evaluated from a single series of aggregate outcomes, and the interrupted time series design estimates the total shift in an outcome after the intervention. When the policy is expected to act through a measurable pathway, the total effect is less informative than its decomposition into direct and indirect effects. We formulate causal mediation for a single interrupted time series and study stabilized mediator weighting as the estimator of the natural direct and indirect effects. Because the intervention is a deterministic function, the exposure weight equals one and the exposure contrast is identified through the segmented-regression level shift, so only the mediator pathway is weighted. We add a cumulative mediator weight that carries the lagged confounder history, incorporate a concurrent event as a second interruption, and replace variance formulas that treat the estimated weights as fixed with a block-residual bootstrap that keeps the deterministic exposure timing intact and resamples the mediator and outcome residuals in moving blocks. In a simulation calibrated to daily data, the unweighted product-of-coefficients estimator is biased under mediator-outcome confounding, with an indirect-effect bias near $0.19$ and coverage of $0.003$, whereas stabilized weighting reduces the bias to about $0.03$ and improves indirect-effect coverage from near zero to about $0.83$. Applied to the 2019 termination of Ontario's Drive Clean vehicle emissions testing program, the method estimates a direct reduction in ground-level ozone of $2.113$ parts per billion (95\% interval $-3.384$ to $-0.841$) that is robust across four Toronto regions, a positive but heterogeneous indirect effect through nitrogen dioxide, and a total effect near the boundary of significance; a pre-pandemic sensitivity analysis agrees with the primary results.
Comments22 Pages, 4 Figures, 8 Tables
