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  "path": "/t/good-reading-on-when-doubly-robust-is-worth-the-effort/17033#post_8",
  "publishedAt": "2026-02-18T09:11:17.000Z",
  "site": "https://discourse.datamethods.org",
  "tags": [
    "PubMed",
    "Adjusting for differential-verification bias in diagnostic-accuracy studies:..."
  ],
  "textContent": "Not exactly\n\nThey try to estimate performance metrics of a prediction model under covariate shift and when the outcome is not available, kind of reminds me de-groot work:\n\nPubMed\n\n### Adjusting for differential-verification bias in diagnostic-accuracy studies:...\n\nIn studies of diagnostic accuracy, the performance of an index test is assessed by verifying its results against those of a reference standard. If verification of index-test results by the preferred reference standard can be performed only in a...\n\nI’m not sure if it is really likely to have a misspecified conditional loss or a misspecified weights for the shift, it seems more likely to me to have unstable performance metrics because of the weights.\n\nLow propensity → high ipw → unstable performance metric. Ideally I would like to see all 3 variations of the correction (conditional loss, weighting, and doubly robust) and judge for my self.\n\nSounds reasonable?",
  "title": "Good reading on when \"doubly robust\" is worth the effort?"
}