They just bite harder in epidemiology because (1) background theory isn't as good at pinpointing relevant causal factors

I've found that there's always a lot of field-specific tricks; it's one of those things I really was hoping to find.

Hmm. Based on the epidemiology papers I've skimmed through over the years, there don't seem to be any killer tricks. The usual procedure for non-experimental papers seems to be to pick a few variables out of thin air that sound like they might be confounders, measure them, and then toss them into a regression alongside the variables one actually cares about. (Sometimes matching is used instead of regression but the idea is similar.)

Still, it's quite possible I'm only drawing a blank because I'm not an epidemiologist and I haven't picked up enough tacit knowledge of useful analysis tricks. Flicking through papers doesn't actually make me an expert.

The really frustrating thing about the lithium-in-drinking-water correlation is that it would be very easy to do a controlled experiment.

True. Even though doing experiments is harder in general in epidemiology, that's a poor excuse for not doing the easy experiments.

I'm interested for generic utilitarian reasons, so I'd be fine with a population-level correlation.

Ah, I see. I misunderstood your earlier comment as being a complaint about population-level correlations.

I'm not sure which variables you're looking for (population-level) correlations among, but my usual procedure for finding correlations is mashing keywords into Google Scholar until I find papers with estimates of the correlations I want. (For this comment, I searched for "smoking IQ conscientiousness correlation" without the quotes, to give an example.) Then I just reuse those numbers for whatever analysis I'd like to do.

This is risky because two variables can correlate differently in different populations. To reduce that risk I try to use the estimate from the population most similar to the population I have in mind, or I try estimating the correlation myself in a public use dataset that happens to include both variables and the population I want.