Probabilistic Programming and Bayesian Inference for Time Series Analysis and Forecasting in Python
July 28, 2020
Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects
Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf
Professor Judea Pearl wrote: “This paper is the first COVID-19 analysis I read that goes beyond data-fitting and tells society: Yes! AI can be trusted to extract meaning from data, and can do so rigorously and practically if properly directed and wisely supported”.
We point out an example of Simpson’s paradox in COVID-19 case fatality rates (CFRs): comparing data from >72,000 cases from China with data from Italy reported on March 9th, we find that CFRs are lower in Italy for each age group, but higher overall. This phenomenon can be explained by a stark difference in case demographic between the two countries. Using this as a motivating example, we review 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 mortality. As a case study, we then investigate how total, direct, and indirect (age-mediated) causal effects between China and Italy evolve over two months until May 7th 2020.
