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  "path": "/t/thinking-clearly-about-association-studies-risk-factors-and-causal-salad-included/28679#post_19",
  "publishedAt": "2026-04-03T13:09:19.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "f2harrell:\n\n> Showing the relative explained variation of a set of potential confounders in predicting treatment choice. It’s amazing how many papers using propensity scores fail to decode the scores.\n\nThanks. I think I might be misunderstanding you here. The example seems to imply, that a descriptive analysis **can** very much have multivariable models whereas in your earlier statement I thought you said they (usually/often) **cannot.** Would you mind clarifying once more for the slow thinker?\n\nf2harrell:\n\n> I don’t see this as a problem. I would not be trying to learn that a specific factor is what causes a treatment to be selected but rather (1) how non-random is the treatment choice and (2) what are the biggest players in predicting treatment choice. The latter could be deemed an association study.\n\nIn the two sections I’m quoting the examples sound descriptive to me but somehow I’m still stumbling over the word “predict”. I might be nit-picking and overthinking… Do you see any issue in your (or the general usage) of using “predict” in this and its widespread context? Or do you think the correct interpretation can be easily assumed from context?",
  "title": "Thinking Clearly about Association Studies (Risk Factors and Causal Salad included)"
}