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.
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.