Unmeasured confounding can severely bias causal effect estimates from spatiotemporal observational data, especially when the confounders do not vary smoothly in time and space. In this work, we develop a method for addressing unmeasured confounding in spatiotemporal contexts by building on models from the panel data literature and methods in multivariate causal inference. Our method is based on a factor confounding assumption, which posits that effects of unmeasured confounders on exposures and outcomes can be captured by a shared latent factor model. Factor confounding is sufficient to partially identify causal effects, even when there is interference between units. Additional assumptions that limit the degree of spatiotemporal interference, reasonable in most applications, are sufficient to point identify the effects. Simulation studies demonstrate that the proposed approach can substantially reduce omitted variable bias relative to other spatial smoothing and panel data baselines. We illustrate our method in a case study of the effect of prenatal PM2.5 exposure on birth weight in California.

翻译：未观测混杂会严重偏倚基于时空观测数据的因果效应估计，尤其当混杂因素在时间与空间维度上呈现非平滑变化时更为显著。本文基于面板数据文献中的模型框架与多元因果推断方法，发展了一种应对时空背景下未观测混杂问题的新方法。该方法建立在因子混杂假设之上，该假设认为未观测混杂因素对暴露变量和结局变量的影响可通过共享潜变量因子模型予以刻画。即使存在单元间干扰效应，因子混杂假设仍足以实现因果效应的部分识别。通过引入在多数应用场景中合理的附加假设来限制时空干扰程度，即可实现因果效应的点识别。仿真研究表明，相较于其他空间平滑方法与面板数据基准模型，所提方法可显著降低遗漏变量偏差。我们以加州产前PM2.5暴露对出生体重影响的案例研究验证了该方法的有效性。