{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:R6WWXKTULXY7IYBIV27ZX6GV64","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7cd4bb0656103d745c86565386c0f8f37d6ebbb07ebbe026b173a30367f14c80","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-08T05:48:07Z","title_canon_sha256":"ea217d65772a775b0dcea9354a98c60b1384ff301f6cae884d57a34756d465ff"},"schema_version":"1.0","source":{"id":"2605.07285","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.07285","created_at":"2026-05-20T00:03:14Z"},{"alias_kind":"arxiv_version","alias_value":"2605.07285v2","created_at":"2026-05-20T00:03:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.07285","created_at":"2026-05-20T00:03:14Z"},{"alias_kind":"pith_short_12","alias_value":"R6WWXKTULXY7","created_at":"2026-05-20T00:03:14Z"},{"alias_kind":"pith_short_16","alias_value":"R6WWXKTULXY7IYBI","created_at":"2026-05-20T00:03:14Z"},{"alias_kind":"pith_short_8","alias_value":"R6WWXKTU","created_at":"2026-05-20T00:03:14Z"}],"graph_snapshots":[{"event_id":"sha256:edca6227b2cb251ebbf7676534f190d03f04b49ec95d240d51cce2df813be433","target":"graph","created_at":"2026-05-20T00:03:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","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."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","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."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","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."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","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."}],"snapshot_sha256":"9cdc96e9f05f4c80138b3d4cea016a8629e114f46abd2d5e5fb704279ff82b4f"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"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"}],"endpoint":"/pith/2605.07285/integrity.json","findings":[],"snapshot_sha256":"c84c9db11e0f6e1dfe1c611a884603cfceb7d4f54159d9577b5ad34e918de33a","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Harrison H Li","cross_cats":[],"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.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-08T05:48:07Z","title":"Transporting treatment effects by calibrating large-scale observational outcomes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.07285","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-11T01:15:29.671505Z","id":"b998368c-bf37-4581-b6c6-66b01523367f","model_set":{"reader":"grok-4.3"},"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","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.","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.","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."}},"verdict_id":"b998368c-bf37-4581-b6c6-66b01523367f"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ea96c290797b9f39ceb8e08c6240a52f412797ffe19bb5c7a3fd7227922d37a5","target":"record","created_at":"2026-05-20T00:03:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7cd4bb0656103d745c86565386c0f8f37d6ebbb07ebbe026b173a30367f14c80","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-08T05:48:07Z","title_canon_sha256":"ea217d65772a775b0dcea9354a98c60b1384ff301f6cae884d57a34756d465ff"},"schema_version":"1.0","source":{"id":"2605.07285","kind":"arxiv","version":2}},"canonical_sha256":"8fad6baa745df1f46028aebf9bf8d5f7273c13d550475c9c4c38d98f9a55ba0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8fad6baa745df1f46028aebf9bf8d5f7273c13d550475c9c4c38d98f9a55ba0d","first_computed_at":"2026-05-20T00:03:14.714087Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:14.714087Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tj1ZYvhfcRIj7nYyYihP4lPtwAk3A2c1HJCEq+iM6Evh3ChV+LfcKOrhmSrUU0osZlFwpYuky5ku1enmcco9Aw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:14.715135Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.07285","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea96c290797b9f39ceb8e08c6240a52f412797ffe19bb5c7a3fd7227922d37a5","sha256:edca6227b2cb251ebbf7676534f190d03f04b49ec95d240d51cce2df813be433"],"state_sha256":"cf3f9c22cd633eb6b1cdbaff0c561682ecf95422046bdd48e2a76f18ff4bb483"}