{"paper":{"title":"Transporting treatment effects by calibrating large-scale observational outcomes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"By calibrating observational outcome measurements to a small experimental dataset via ordinary least squares, researchers obtain a consistent estimator for the transported average treatment effect with valid inference even without overlap.","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Harrison H Li","submitted_at":"2026-05-08T05:48:07Z","abstract_excerpt":"A high-quality experimental dataset is often much smaller than a corresponding observational dataset. When this holds with possibly biased measurements of the outcome of interest in the latter, we propose an estimation and inference procedure for a transported treatment effect. Our point estimator can be computed as follows. First, we estimate the conditional average treatment effect (CATE) by calibrating a treatment-control contrast estimated using the observational outcomes to the experimental dataset using ordinary least squares (OLS). Then, we compute the sample average of this estimated C"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our estimator is consistent for the transported average treatment effect. Otherwise, it converges to a projection estimand. As long as the observational dataset size grows sufficiently quickly relative to the experimental dataset size, our estimator achieves a notion of semiparametric efficiency proposed in recent work on semi-supervised learning for the projection estimand.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"When the calibration regression is well specified for consistency; also that the observational dataset size grows sufficiently quickly relative to the experimental dataset size for efficiency, and that OLS calibration can handle biased measurements in the observational outcome.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"By calibrating observational outcome measurements to a small experimental dataset via ordinary least squares, researchers obtain a consistent estimator for the transported average treatment effect with valid inference even without overlap.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9cdc96e9f05f4c80138b3d4cea016a8629e114f46abd2d5e5fb704279ff82b4f"},"source":{"id":"2605.07285","kind":"arxiv","version":2},"verdict":{"id":"b998368c-bf37-4581-b6c6-66b01523367f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T01:15:29.671505Z","strongest_claim":"our estimator is consistent for the transported average treatment effect. Otherwise, it converges to a projection estimand. As long as the observational dataset size grows sufficiently quickly relative to the experimental dataset size, our estimator achieves a notion of semiparametric efficiency proposed in recent work on semi-supervised learning for the projection estimand.","one_line_summary":"Proposes a calibration-based estimator for transported average treatment effects that is consistent under correct specification and achieves semiparametric efficiency with large observational data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"When the calibration regression is well specified for consistency; also that the observational dataset size grows sufficiently quickly relative to the experimental dataset size for efficiency, and that OLS calibration can handle biased measurements in the observational outcome.","pith_extraction_headline":"By calibrating observational outcome measurements to a small experimental dataset via ordinary least squares, researchers obtain a consistent estimator for the transported average treatment effect with valid inference even without overlap."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.07285/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T17:01:19.077385Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T11:54:26.312517Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"c84c9db11e0f6e1dfe1c611a884603cfceb7d4f54159d9577b5ad34e918de33a"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}