Statistical Modeling, Causal Inference, and Social Science

I [Morris] have downloaded and evaluated the recent Israeli data in detail to explore what it tells us about efficacy vs. severe disease with the Delta variant.

In spite of the fact that ~60% of those with severe infections are vaccinated (as emphasized by anti-vaxxers as well as people pushing third dose boosters), the data clearly show the efficacy vs. severe disease is 85-90% in both younger and older age groups.

I have written an article on my covid-datascience.com blog clearly explaining this paradoxical result step by step.

My explanation illustrates the erroneous arguments made by people who just compare raw counts in discussing vaccine efficacy, and also highlights how Simpson’s paradox rears its ugly head here given older people are both more vaccinated and have inherently higher risk of hospitalization and thus any overall efficacy results produces misleading results if not stratified by age. Morris’s one-sentence summary: Many are confused by results that >1/2 of hospitalized in Israel are vaccinated, thinking this means vaccines don’t work. I [Morris] downloaded actual Israeli data and show why these data provide strong evidence vaccines strongly protect vs. serious disease. I agree with Morris that this is a policy-relevant example of a general statistics principle.

My explanation illustrates the erroneous arguments made by people who just compare raw counts in discussing vaccine efficacy, and also highlights how Simpson’s paradox rears its ugly head here given older people are both more vaccinated and have inherently higher risk of hospitalization and thus any overall efficacy results produces misleading results if not stratified by age.

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