新冠肺炎病死率中的辛普森悖论：年龄相关因果效应的中介分析
Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects
摘要：
我们指出辛普森悖论在新冠肺炎病死率（CFR）中的一个实例：将中国的一项大规模研究（2月17日）与意大利的早期报告（3月9日）进行比较，我们发现意大利每个年龄组的CFR都较低，但总体较高。这一现象的原因是两国人口统计学上的明显差异。以此为例，我们介绍了中介分析的基本概念，并展示了当假设一个涉及国家、年龄和病死率的粗粒度因果图时，这些概念如何用于量化不同的直接和间接影响。我们策划了一个年龄分层的CFR数据集，其中包含超过75万例病例，并进行了一项案例研究，调查了不同国家和不同时间点之间的总的、直接的和间接的（年龄介导的）因果效应。这使我们能够将与年龄相关的影响与其他与年龄无关的影响分开，并有助于更透明地比较处于COVID-19大流行不同阶段的国家的CFR。使用意大利的纵向数据，我们发现3月中旬的直接因果效应出现了符号反转，这与该国部分地区报告的医疗保健系统崩溃在时间上一致。此外，我们发现，132对国家的直接和间接影响只有微弱的相关性，这表明一个国家的政策和病例人口统计可能在很大程度上是无关的。我们指出了未来工作的局限性和扩展，最后，讨论了因果推理在使用人工智能对抗新冠肺炎大流行的更广泛背景下的作用。
英文摘要：
We point out an instantiation of Simpson's paradox in Covid-19 case fatality rates (CFRs): comparing a large-scale study from China (17 Feb) with early reports from Italy (9 Mar), we find that CFRs are lower in Italy for every age group, but higher overall. This phenomenon is explained by a stark difference in case demographic between the two countries. Using this as a motivating example, we introduce basic concepts from mediation analysis and show how these can be used to quantify different direct and indirect effects when assuming a coarse-grained causal graph involving country, age, and case fatality. We curate an age-stratified CFR dataset with >750k cases and conduct a case study, investigating total, direct, and indirect (age-mediated) causal effects between different countries and at different points in time. This allows us to separate age-related effects from others unrelated to age and facilitates a more transparent comparison of CFRs across countries at different stages of the Covid-19 pandemic. Using longitudinal data from Italy, we discover a sign reversal of the direct causal effect in mid-March which temporally aligns with the reported collapse of the healthcare system in parts of the country. Moreover, we find that direct and indirect effects across 132 pairs of countries are only weakly correlated, suggesting that a country's policy and case demographic may be largely unrelated. We point out limitations and extensions for future work, and, finally, discuss the role of causal reasoning in the broader context of using AI to combat the Covid-19 pandemic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
