Causal explanations, error rates, and human judgment biases missing from the COVID-19 narrative and statistics

Last week we wrote about the importance of causal explanations for differences between countries' COVID-19 death rates, and the need for more random testing. Following on from that we now explain the importance of causal modelling in understanding the results of different types of COVID-19 testing in order to expose what is lacking and what is needed to…