论文标题
Weather2VEC：在空气污染和气候研究中与非本地混淆的因果推断的代表性学习
Weather2vec: Representation Learning for Causal Inference with Non-Local Confounding in Air Pollution and Climate Studies
论文作者
论文摘要
估计空间变化的干预对空间变化结果的因果关系可能会受到非本地混杂（NLC）的影响，这种现象可能会估计给定单位的处理和结果部分由附近单元的其他协方差所指示。特别是，NLC是评估环境政策和气候事件对健康相关结果（例如空气污染暴露）的影响的挑战。本文首先使用潜在结果框架对NLC进行了形式化，从而与因果干扰的相关现象进行了比较。然后，它提出了一个称为“ weather2vec”的广泛适用框架，该框架使用平衡分数的理论将非本地信息的表示形式学习为针对每个观察单元定义的标量或矢量，随后被用于与因果界定方法结合进行混淆。在一项模拟研究中评估了该框架，并进行了两项关于天气（本质上）已知混杂因素的空气污染的案例研究。
Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are dictated in part by the covariates of other nearby units. In particular, NLC is a challenge for evaluating the effects of environmental policies and climate events on health-related outcomes such as air pollution exposure. This paper first formalizes NLC using the potential outcomes framework, providing a comparison with the related phenomenon of causal interference. Then, it proposes a broadly applicable framework, termed "weather2vec", that uses the theory of balancing scores to learn representations of non-local information into a scalar or vector defined for each observational unit, which is subsequently used to adjust for confounding in conjunction with causal inference methods. The framework is evaluated in a simulation study and two case studies on air pollution where the weather is an (inherently regional) known confounder.
