Can we characterize better this area where correlations are especially suspect?
Epidemiological studies of diets (that is, health consequences of particular patterns of food intake) are all based on correlations and the great majority of them is junk.
These days epi people mostly use g methods which are not junk (or rather, give correct answers given assumptions they make, and are quite a bit more sophisticated than just using conditional probabilities). How much epi do you know?
edit: Correction: not everyone uses g methods. There is obviously the “changing of the guard” issue. But g methods are very influential now. I also agree there is a lot of junk in data analysis. But I think the “junk” issue is not always (or even usually) due to the fact that the study was “based on correlations” (you are not being precise about what you mean here, but I interpreted you to mean that “people are not using correct methods for getting causal conclusions from observational data.”)
Not much. I’ve read a bunch of papers and some critiques… And I’m talking not so much about the methods as about the published claims and conclusions. Sophisticated methods are fine, the issue is their fragility. And, of course, you can’t correct for what you don’t know.
Epidemiological studies of diets (that is, health consequences of particular patterns of food intake) are all based on correlations and the great majority of them is junk.
These days epi people mostly use g methods which are not junk (or rather, give correct answers given assumptions they make, and are quite a bit more sophisticated than just using conditional probabilities). How much epi do you know?
edit: Correction: not everyone uses g methods. There is obviously the “changing of the guard” issue. But g methods are very influential now. I also agree there is a lot of junk in data analysis. But I think the “junk” issue is not always (or even usually) due to the fact that the study was “based on correlations” (you are not being precise about what you mean here, but I interpreted you to mean that “people are not using correct methods for getting causal conclusions from observational data.”)
Not much. I’ve read a bunch of papers and some critiques… And I’m talking not so much about the methods as about the published claims and conclusions. Sophisticated methods are fine, the issue is their fragility. And, of course, you can’t correct for what you don’t know.