Causal Mediation Analysis for an Interrupted Time Series: Stabilized Mediator Weighting with an Application to a Vehicle Emissions Policy
中断时间序列的因果中介分析：稳定中介加权及车辆排放政策应用AI 解读 ↓
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中断时间序列的因果中介分析：稳定中介加权及车辆排放政策应用
- 研究问题
- 仅有一条总体结果时间序列时，如何将固定时点实施的政策效应分解为自然直接效应与间接效应，并处理中介路径混杂？
- 方法与思路
- 作者结合分段回归水平变化与稳定中介加权，纳入滞后混杂历史和第二次中断，再用移动区块残差自助法处理估计权重与时序相关。
- 材料陈述的贡献
- 摘要报告，加权方法在校准模拟中减少间接效应偏差并改善覆盖率；安大略政策应用显示臭氧直接下降，中介效应存在地区异质性。
- 阅读关注点
- 应核对自然效应的识别条件、确定性政策时点如何支持反事实比较、累积权重的构造，以及并发事件和区块长度的处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未完整说明识别条件，报告的覆盖率改善也不等于达到名义覆盖率。
原文摘要
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.