论文标题
共同19.29在德国传播的因果分析
Causal analysis of Covid-19 spread in Germany
论文作者
论文摘要
在这项工作中，我们研究了自大流行开始以来Covid-19的蔓延，研究了德国地区之间的因果关系，并考虑到不同联邦国家应用的限制政策。我们提出并证明了用于时间序列数据的因果特征选择方法的新定理，对潜在混杂因素的强大定理，随后我们将其应用于COVID-19案例编号。我们提出了有关该病毒在德国的传播以及限制措施的因果影响的发现，并讨论了各种政策在包含扩散中的作用。由于我们的结果基于目标时间序列相当有限（仅报告的案例数），因此应在解释它们时要注意。但是，令人鼓舞的是，已经有限的数据似乎包含因果信号。这表明，随着越来越多的数据可用，我们的因果方法可能有助于对Covid-19的发展政治干预措施有意义的因果分析，从而有助于开发选择干预措施的理性和数据驱动方法。
In this work, we study the causal relations among German regions in terms of the spread of Covid-19 since the beginning of the pandemic, taking into account the restriction policies that were applied by the different federal states. We propose and prove a new theorem for a causal feature selection method for time series data, robust to latent confounders, which we subsequently apply on Covid-19 case numbers. We present findings about the spread of the virus in Germany and the causal impact of restriction measures, discussing the role of various policies in containing the spread. Since our results are based on rather limited target time series (only the numbers of reported cases), care should be exercised in interpreting them. However, it is encouraging that already such limited data seems to contain causal signals. This suggests that as more data becomes available, our causal approach may contribute towards meaningful causal analysis of political interventions on the development of Covid-19, and thus also towards the development of rational and data-driven methodologies for choosing interventions.
