Marginal Structural Models for Electricity Demand under Treatment-Confounder Feedback: A Continuous-Treatment Outcome-Adaptive and Fused LASSO Approach
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处理—混杂反馈下电力需求的边际结构模型与连续处理自适应 LASSO
- 研究问题
- 过去气温影响未来天气、天气又同时影响当前气温与电力需求时，如何在单条时间序列中估计气温的因果效应？
- 方法与思路
- 作者以逆概率加权边际结构模型处理反馈，将结果自适应与自适应融合 LASSO 扩展至连续处理，使用密度比权重和协方差平衡。
- 材料陈述的贡献
- 摘要报告模拟中加权估计的偏差与覆盖率改善；安大略应用显示单滞后调整下正向二次效应，但累积估计受正值性限制。
- 阅读关注点
- 应核对连续处理的时序识别假设、密度比估计与时间依赖推断，以及有效样本量、共线性和正值性诊断如何支持结论。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要给出数值但未说明效应单位与完整条件，且自适应选择不能消除正值性问题。
原文摘要
Temperature is the most important meteorological driver of electricity demand, but its causal effect has not been estimated under the time-varying confounding that characterizes weather processes. Current temperature is associated with weather conditions, including precipitation, snow cover, and cloud cover, that also influence electricity demand, and past temperature shapes future weather, producing treatment-confounder feedback under which standard regression adjustment is biased, due to conditioning on a time-varying confounder. We estimate the causal effect of temperature on daily Ontario electricity demand from 2018 to 2019 using a marginal structural model with inverse-probability weighting, formulated for a single observed time series rather than a panel of independent subjects, and we extend the longitudinal outcome-adaptive LASSO and adaptive fused LASSO, previously developed for binary treatments, to a continuous treatment using density-ratio weights and a weighted-covariance balance criterion. In a Monte Carlo study, unadjusted regression is biased toward the null with coverage of $0.12$ to $0.15$, whereas the stabilized outcome-adaptive estimators are nearly unbiased with coverage near $0.92$, and the cumulative three-day estimators are approximately unbiased but less efficient. In the Ontario data all estimators identify a positive and highly significant quadratic temperature effect; the unadjusted estimate is $9.34$, the stabilized and outcome-adaptive single-lag estimators give $9.8$ to $10.1$, and the cumulative estimators give smaller values that coincide with a sharp fall in effective sample size and near-unit air-density collinearity. The temperature effect on demand is therefore large and robust to single-lag confounding adjustment, while the cumulative estimates are compromised by positivity limitation that outcome-adaptive selection cannot remove.