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  "path": "/t/thinking-clearly-about-association-studies-risk-factors-and-causal-salad-included/28679?page=2#post_27",
  "publishedAt": "2026-04-04T14:19:47.000Z",
  "site": "https://discourse.datamethods.org",
  "textContent": "Yes I didn’t word that accurately, I was primarily extrapolating from the paper. Adjustment roles in descriptive inference improving precision by accounting for covariate related variation, and in prediction, where causal structure is irrelevant and inclusion is justified by predictive performance.\n\nThe context I had in mind was narrower: the observational study with a specific named exposure, a disease outcome, and an adjustment set chosen without stating justification what Kezios calls the ‘seemingly causal’ study. In that setting, adjusting for Z while asking specifically about X→Y is hard to interpret as anything other than an implicit causal operation, because the only coherent reason to condition on Z in that context — rather than simply describing Y or predicting Y from all available variables equally is to isolate the X-Y relationship from Z’s influence.",
  "title": "Thinking Clearly about Association Studies (Risk Factors and Causal Salad included)"
}