CAUSAL INFERENCE FOR THE EFFECT OF MOBILITY ON COVID-19 DEATHS
CAUSAL INFERENCE FOR THE EFFECT OF MOBILITY ON COVID-19 DEATHS
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DOI：
10.1214/22-aoas1599
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发表时间：
2022-12-01
影响因子：
1.8
通讯作者：
Wasserman, Larry
中科院分区：
文献类型：
--
作者：
Bonvini, Matteo;Kennedy, Edward H.;Wasserman, Larry
在本文中，我们开发了流行病因果推断的统计方法。我们的重点是估计 Covid-19 大流行第一年社会流动性对死亡的影响。我们提出了一个由基本流行病模型驱动的边际结构模型。我们估计了流动性干预措施下死亡的反事实时间序列。我们进行几种类型的敏感性分析。我们发现数据支持流动性减少会导致死亡人数减少的观点，但结论也有一些警告。有证据表明对模型错误指定和未测量的混杂因素很敏感，这意味着需要谨慎解释因果效应的大小。尽管毫无疑问这种影响是真实的，但我们的工作凸显了从大流行数据中得出因果推论的挑战。
In this paper we develop statistical methods for causal inference in epi-demics. Our focus is in estimating the effect of social mobility on deaths in the first year of the Covid-19 pandemic. We propose a marginal structural model motivated by 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 mo-bility causes reduced deaths, but the conclusion comes with caveats. There is evidence of sensitivity to model misspecification and unmeasured confound-ing which implies that the size of the causal effect needs to be interpreted with caution. While there is little doubt the effect is real, our work highlights the challenges in drawing causal inferences from pandemic data.