Tensor Completion for Causal Inference with Multivariate Longitudinal Data: A Reevaluation of COVID-19 Mandates
多变量纵向数据因果推断的张量补全：重新评估疫情管制措施AI 解读 ↓
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多变量纵向数据因果推断的张量补全：重新评估疫情管制措施
- 研究问题
- 如何利用同一单位与时期的多个相关结果改进反事实插补，并据此评估旅行限制、口罩指令和疫苗要求等政策的死亡影响？
- 方法与思路
- 作者将单位、时间和结果组织为三维张量，通过张量补全插补缺失反事实，建立多结果合并的准确性结论并比较政策评估方法。
- 材料陈述的贡献
- 摘要称，多结果合并在一般条件下提高插补准确性；应用认为其他方法高估口罩指令效果，且其不能有效替代旅行限制。
- 阅读关注点
- 核对张量结构与补全条件、政策效应的识别依据、其他结果是否受政策影响，以及同时实施政策和时变混杂如何处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未列明理论条件和政策识别设计，所述政策结论不能直接视为已验证事实。
原文摘要
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.