AI 中文总结
本研究针对存在处理-混杂因子反馈的电力需求场景，将结果自适应融合LASSO扩展至连续处理，通过边际结构模型结合逆概率加权法，准确估计温度对安大略省电力需求的因果效应。
AI 中文摘要
温度是电力需求最重要的气象驱动因素，但在表征天气过程的时变混杂作用下，其因果效应尚未得到估计。当前温度与降水、积雪覆盖、云量等同样影响电力需求的天气状况相关，而过往温度会塑造未来天气，从而产生处理-混杂因子反馈，在此情况下，标准回归调整因对时变混杂因子进行条件设定而存在偏差。我们采用边际结构模型结合逆概率加权法，估计2018至2019年安大略省每日电力需求受温度影响的因果效应，该模型针对单一观测时间序列而非独立主体面板构建；我们将此前针对二元处理开发的纵向结果自适应LASSO和自适应融合LASSO扩展至连续处理，采用密度比权重和加权协方差平衡准则。在蒙特卡洛研究中，未调整回归存在向零值的偏差，覆盖率为0.12至0.15；而稳定化结果自适应估计量几乎无偏，覆盖率接近0.92；累积三天估计量近似无偏，但效率较低。在安大略省数据中，所有估计量均识别出显著为正的二次温度效应：未调整估计值为9.34，稳定化及结果自适应单滞后估计量给出9.8至10.1，累积估计量因有效样本量急剧下降及接近单位的空气密度共线性给出更小值。因此，温度对电力需求的影响较大，且对单滞后混杂调整具有鲁棒性，而累积估计量因结果自适应选择无法消除的正性限制而受到损害。
英文摘要
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.
Comments26 Pages, 4 Figures, 4 Tables
