论文标题
面具和COVID-19：将价值归因于公共卫生干预措施的因果框架
Masks and COVID-19: a causal framework for imputing value to public-health interventions
论文作者
论文摘要
在Covid-19大流行期间，科学界开发了预测模型来评估潜在的政府干预措施。但是，对这些干预措施的影响的分析较少。在这里，我们提出了一个数据驱动的框架，以回顾性地评估这些效果。我们使用正则回归来找到一个简约的模型，该模型适合RT参数变化最小的数据。然后，我们假设RT中的每一个跳跃是干预的效果。按照Do-oserator处方，我们通过强迫RT保持在跳跃前值来模拟反事实情况。然后，我们将一个值归因于真正的进化与模拟反事实之间的差异。我们表明，在170000（95％CI 160000至180000年）在康涅狄格州，马萨诸塞州和纽约州使用面部面具的建议将减少170000年的案件数量（95％CI 160000至180000）。在任何情况下，及时的因素和效果稀疏的任何情况下，这里介绍的框架都可以使用。
During the COVID-19 pandemic, the scientific community developed predictive models to evaluate potential governmental interventions. However, the analysis of the effects these interventions had is less advanced. Here, we propose a data-driven framework to assess these effects retrospectively. We use a regularized regression to find a parsimonious model that fits the data with the least changes in the Rt parameter. Then, we postulate each jump in Rt as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing Rt to stay at the pre-jump value. We then attribute a value to the intervention from the difference between true evolution and simulated counterfactual. We show that the recommendation to use facemasks for all activities would reduce the number of cases by 170000 (95% CI 160000 to 180000) in Connecticut, Massachusetts, and New York State. The framework presented here might be used in any case where cause and effects are sparse in time.
