In this paper we develop statistical methods for causal inference in epidemics. Our focus is in estimating the effect of social mobility on deaths in the Covid-19 pandemic. We propose a marginal structural model motivated by a modified version of a basic epidemic model. We estimate the counterfactual time series of deaths under interventions on mobility. We conduct several types of sensitivity analyses. We find that the data support the idea that reduced mobility causes reduced deaths, but the conclusion comes with caveats. There is evidence of sensitivity to model misspecification and unmeasured confounding which implies that the size of the causal effect needs to be interpreted with caution. While there is little doubt the the effect is real, our work highlights the challenges in drawing causal inferences from pandemic data.

翻译：在本文中,我们为流行病的因果关系推断制定了统计方法。我们的重点是估计社会流动性对Covid-19大流行中死亡的影响。我们提出了一个边缘结构模型,其动因是基本流行病模型的修改版本。我们估计了流动干预下死亡的反事实时间序列。我们进行了几种敏感性分析。我们发现,数据支持了减少流动性导致死亡的理念,但结论带有警告性。有证据表明,对模型的错误区分和未测量的混杂十分敏感,这意味着对因果关系的大小需要谨慎解释。尽管效果是真实的,但我们的工作突出了从大流行病数据中得出因果关系推断方面的挑战。