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：未观测混杂因素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空维度上非平滑变化时更为显著。本研究通过融合面板数据文献中的模型与多变量因果推断方法，发展了一种处理时空背景下未观测混杂的技术。该方法基于因子混杂假设，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捕捉。即使存在单元间干扰，因子混杂假设仍足以实现因果效应的部分识别；而进一步限制时空干扰程度的附加假设（这在大多数应用中具有合理性）则足以实现点识别。模拟研究表明，相较于其他空间平滑法和面板数据基准方法，本方法能显著降低遗漏变量偏误。我们通过加利福尼亚州产前PM2.5暴露对出生体重影响的案例研究展示了该方法的实际应用。