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 reduce the uncertainty in classifying an individual as infected with COVID-19.
The full report is here:
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