{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:R6WWXKTULXY7IYBIV27ZX6GV64","short_pith_number":"pith:R6WWXKTU","schema_version":"1.0","canonical_sha256":"8fad6baa745df1f46028aebf9bf8d5f7273c13d550475c9c4c38d98f9a55ba0d","source":{"kind":"arxiv","id":"2605.07285","version":2},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.07285","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-08T05:48:07Z","cross_cats_sorted":[],"title_canon_sha256":"ea217d65772a775b0dcea9354a98c60b1384ff301f6cae884d57a34756d465ff","abstract_canon_sha256":"7cd4bb0656103d745c86565386c0f8f37d6ebbb07ebbe026b173a30367f14c80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:14.715135Z","signature_b64":"tj1ZYvhfcRIj7nYyYihP4lPtwAk3A2c1HJCEq+iM6Evh3ChV+LfcKOrhmSrUU0osZlFwpYuky5ku1enmcco9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fad6baa745df1f46028aebf9bf8d5f7273c13d550475c9c4c38d98f9a55ba0d","last_reissued_at":"2026-05-20T00:03:14.714087Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:14.714087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.07285","created_at":"2026-05-20T00:03:14.714231+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.07285v2","created_at":"2026-05-20T00:03:14.714231+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.07285","created_at":"2026-05-20T00:03:14.714231+00:00"},{"alias_kind":"pith_short_12","alias_value":"R6WWXKTULXY7","created_at":"2026-05-20T00:03:14.714231+00:00"},{"alias_kind":"pith_short_16","alias_value":"R6WWXKTULXY7IYBI","created_at":"2026-05-20T00:03:14.714231+00:00"},{"alias_kind":"pith_short_8","alias_value":"R6WWXKTU","created_at":"2026-05-20T00:03:14.714231+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64","json":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64.json","graph_json":"https://pith.science/api/pith-number/R6WWXKTULXY7IYBIV27ZX6GV64/graph.json","events_json":"https://pith.science/api/pith-number/R6WWXKTULXY7IYBIV27ZX6GV64/events.json","paper":"https://pith.science/paper/R6WWXKTU"},"agent_actions":{"view_html":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64","download_json":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64.json","view_paper":"https://pith.science/paper/R6WWXKTU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.07285&json=true","fetch_graph":"https://pith.science/api/pith-number/R6WWXKTULXY7IYBIV27ZX6GV64/graph.json","fetch_events":"https://pith.science/api/pith-number/R6WWXKTULXY7IYBIV27ZX6GV64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64/action/storage_attestation","attest_author":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64/action/author_attestation","sign_citation":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64/action/citation_signature","submit_replication":"https://pith.science/pith/R6WWXKTULXY7IYBIV27ZX6GV64/action/replication_record"}},"created_at":"2026-05-20T00:03:14.714231+00:00","updated_at":"2026-05-20T00:03:14.714231+00:00"}