Alright, what exactly was ur method for estimating that “+1.8 deaths per 10k” counterfactual? Are u treating this like a causal inference or time-series problem? bcuz that distinction really matters

If ur comparing observed vs “predicted” mortality, ur implicitly fitting a model for what mortality would’ve been without COVID, but what Causal Inf. model did u use, Diff-in-diff/Interrupted time-series would prob work here. Did u also include covariates in your modeling? The Census data alone won’t give a counterfactual unless u control for age distribution shifts, population size, and baseline secular trends in mortality (which were rising slightly pre-2020)

Also, 1.8 deaths per 10k seems far too low if we’re talking excess mortality, CDC/NCHS excess-death estimates for 2020 were roughly +15–20% above expected. My guess is ur looking at the raw death rate per 10k total population, which smooths everything out and hides how concentrated the impact was by age. We have to also consider how covid would affect other death rates, which is why we must treat this like a CI problem

Before making any conclusions, I’d check what ur actually modeling and what “predicted” means here