论文标题
通过多元纵向数据进行因果推断的张量完成：重新评估COVID-19授权
Tensor Completion for Causal Inference with Multivariate Longitudinal Data: A Reevaluation of COVID-19 Mandates
论文作者
论文摘要
我们提出了一种使用张量完成的新方法来估计具有多元纵向数据的因果效应，该数据在每个单元和时间段中都观察到多个结果。我们的动机是估计政府授权（例如旅行限制，戴面具指令和疫苗接种要求）预防的共同死亡人数。除199年的死亡人数外，我们还观察到相关的结果，例如其他疾病和伤害的死亡人数。所提出的方法将数据作为张量，具有三个维度（单位，时间和结果），并使用张量完成来归咎于缺失的反事实结果。我们首先证明，在一般条件下，使用所提出的方法结合多个结果可提高反事实归咎于的准确性。然后，我们将提出的方法与通常用于评估Covid-19授权的其他方法进行比较。我们的主要发现是，其他方法高估了戴掩盖指令的效果，并且戴面具指令不是旅行限制的有效替代方案。我们得出的结论是，尽管有多元纵向数据可用，但可以应用所提出的方法，但我们认为这尤其及时，因为政府越来越依赖纵向数据来在公共卫生紧急情况下等政策中选择纵向数据。
We propose a new method that uses tensor completion to estimate causal effects with multivariate longitudinal data, data in which multiple outcomes are observed for each unit and time period. Our motivation is to estimate the number of COVID-19 fatalities prevented by government mandates such as travel restrictions, mask-wearing directives, and vaccination requirements. In addition to COVID-19 fatalities, we observe related outcomes such as the number of fatalities from other diseases and injuries. The proposed method arranges the data as a tensor with three dimensions (unit, time, and outcome) and uses tensor completion to impute the missing counterfactual outcomes. We first prove that under general conditions, combining multiple outcomes using the proposed method improves the accuracy of counterfactual imputations. We then compare the proposed method to other approaches commonly used to evaluate COVID-19 mandates. Our main finding is that other approaches overestimate the effect of masking-wearing directives and that mask-wearing directives were not an effective alternative to travel restrictions. We conclude that while the proposed method can be applied whenever multivariate longitudinal data are available, we believe it is particularly timely as governments increasingly rely on longitudinal data to choose among policies such as mandates during public health emergencies.
