发表机构
Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对气候科学中时空依赖与处理高维导致的因果效应估计难题，提出时空随机干预框架及正则化估计器，降低均方误差并消除混杂偏差，应用于历史变暖对蒸汽压亏缺的影响估计，发现降水调整显著改变结果。
AI 中文摘要
在气候科学中估计因果效应，例如人为变暖对作物损失的影响，因复杂的时空依赖性和处理的高维特性而具有挑战性。为解决这种依赖性以及观测情景与反事实情景之间由此产生的较差重叠，我们开发了一个时空随机干预框架，用于从气候观测中估计因果效应。我们引入了一个随机干预处理效应的正则化估计器，该估计器以受控偏差换取因较差重叠导致的权重方差的减少。模拟研究表明，该估计器比替代加权估计器实现了更低的均方误差，并消除了未调整估计器的混杂偏差。我们将该框架应用于估计历史变暖对蒸汽压亏缺（作物胁迫的一个驱动因素）的影响，并调整降水，降水通过影响温度和湿度而混淆了该效应。在GISS-E2-1-G气候模型模拟中，从1995年起，全球效应在每一年都与零有显著区别，而省略降水调整会使全球估计值膨胀47%。调整在8%的全球农田（1.25亿公顷）上逆转了估计值的符号，在这些地区，未调整的分析可能在以热量为重点和以水分为重点的适应措施之间误导决策。
英文摘要
Estimating causal effects in climate science, such as the effect of anthropogenic warming on crop loss, is challenging because of complex spatio-temporal dependence and the high-dimensional nature of the treatment. To address this dependence and the resulting poor overlap between observed and counterfactual scenarios, we develop a spatio-temporal stochastic-intervention framework for estimating causal effects from climate observations. We introduce a regularized estimator of the stochastic-intervention treatment effect that trades a controlled bias for a reduction in the weight variance caused by poor overlap. Simulation studies show that this estimator attains lower mean squared error than alternative weighting estimators and removes the confounding bias of an unadjusted estimator. We apply the framework to estimate the effect of historical warming on vapor-pressure deficit, a driver of crop stress, adjusting for precipitation, which confounds the effect by affecting both temperature and humidity. In GISS-E2-1-G climate-model simulations, the global effect is distinguishable from zero in every year from 1995 onward, and omitting the precipitation adjustment inflates the global estimate by 47%. Adjustment reverses the sign of the estimate over 8% of global cropland (125 million hectares), where an unadjusted analysis could misdirect adaptation between heat-focused and moisture-focused measures.
Comments34 pages, 13 figures, 7 tables; includes the supplementary material. Package: https://github.com/samjbaugh/stsi; analysis code: https://github.com/samjbaugh/stsi_analysis
