Flu vaccination linked to 40% reduced risk of Alzheimer’s disease

1. The journal article didn't suggest it was causal. But such a correlation with such a large population warrants publication and further research into causation.

2. literally the first thing that any epidemiologist would consider is potential confounders. There is a big list of covariates they included into their model here: https://content.iospress.com/articles/journal-of-alzheimers-...

There are quite a few things that can be done to alleviate potential false correlations: DAGs, prior literature, removing confounders, and including covariates are all things at disposal.

3. Such a large sample size + previously reported findings + an inclusion of enough covariates still doesn't == causation, BUT it's important to publish and shout about so we can then look into the potential biological underpinnings that may cause this. Which by the way, those experiments may still use data science techniques.

4. If you are actually interested, there is a whole topic of this called 'causal inference' with one famous criteria list called the 'Bradford Hill Criteria': https://en.wikipedia.org/wiki/Bradford_Hill_criteria. This list is often argued about.

5. If all of this information was new to you, please stop spouting 'correlation != causation'. You probably don't know as much as you think

As a physicist who had to endure helping biologists with statistics and cooling down their enthusiasm: it make sense to have a deeper thought about the experiment.

As parent wrote, there may be various reasons for the correlations, sometimes you have random stuff, sometimes indirect stuff and in others extraordinary stuff. Many discoveries (especially older ones) fall into the last category.

That hardly seems relevant, since we're talking about causal relationships and chance is the opposite of causation.