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

Epidemiologist in training here... There are quite a few comments in this thread already jumping on the 'correlation != causation' train. While that is true, I'd like to clarify a couple things:

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

I do not remember where I read it (probably xkcd) but it was that correlation is not casuation, though the numbers are doing big winks to you (or something like that)

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.

I like the phrasing, "Correlation correlates with causation because causation causes correlations".

Causation frequently removes correlations that would otherwise exist.

I don't think that's right? In theory you could have A and B that were naturally correlated and add a causal relationship between the two that exactly canceled that correlation but that would be infinity unlikely to happen by chance. In practice the only way that A and B can be correlated is that either A causes B, B causes A, or some C causes both A and B. Interestingly if both A and B cause C the C and A as well as C and B will be correlated but A and B won't be, allowing you to work out causation from a strictly correlational graph.