{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:Q5RMJMIFPGMS5GYYUAVOY3YSDV","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":"ee19ff875ac76f6263b008403213cc73533f7cc1faa8116e2efb47d72658e6f3","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-05-14T02:19:41Z","title_canon_sha256":"2b69707950ae304da74973cd88d48a2cd1dbdf667e2916317ad0636400470530"},"schema_version":"1.0","source":{"id":"2605.14275","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14275","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14275v1","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14275","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"pith_short_12","alias_value":"Q5RMJMIFPGMS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"Q5RMJMIFPGMS5GYY","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"Q5RMJMIF","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:a676bcf855795f8310a9122327475e6b20367d1da349f4e431d82133d9b36d4f","target":"graph","created_at":"2026-05-17T23:39:10Z","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":"Under treatment randomization, positivity, and a surrogate-based transportability assumption, we establish identification and develop a doubly robust estimator for inference. The estimator accommodates flexible machine learning methods for nuisance estimation, remains consistent if either the score-related or outcome regression-related nuisance functions are consistently estimated, and is asymptotically normal under regularity conditions."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The surrogate-based transportability assumption that permits linking short-term surrogates observed in the randomized trial to long-term outcomes in the external observational data."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A doubly robust estimator identifies quantile treatment effects on long-term outcomes by linking trial surrogates to external data."}],"snapshot_sha256":"b7bca327ac8a58b51a4934fd1406bb57c5d545436eea522d94d6f671d571ace9"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Long-term outcomes are often unavailable in randomized clinical trials, although short-term surrogate outcomes are commonly observed. External observational data may contain the long-term outcome, but causal comparisons based on such data alone are vulnerable to confounding. Existing surrogate-based data integration methods for long-term outcomes have focused primarily on average treatment effects. We study estimation of quantile treatment effects for long-term outcomes in the trial population by combining randomized trial data with external observational data. Under treatment randomization, p","authors_text":"Niwen Zhou, Peng Wu, Xu Guo, Ziyang Liu","cross_cats":["stat.TH"],"headline":"A doubly robust estimator identifies quantile treatment effects on long-term outcomes by linking trial surrogates to external data.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-05-14T02:19:41Z","title":"Double/debiased machine learning of quantile treatment effects on long-term outcomes in clinical trials"},"references":{"count":33,"internal_anchors":0,"resolved_work":33,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Double/debiased machine learning for treatment and structural parameters , author=. 2018 , publisher=","work_id":"8ccbab19-be51-4b8b-b547-d37977f8ca39","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Gaussian approximation of suprema of empirical processes , author=","work_id":"664ad426-5422-411b-b019-0b8cb00c0cbe","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Handbook of econometrics , volume=","work_id":"8c8aad9f-0ead-4e5b-a389-2c69126aae34","year":1994},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Weak convergence , author=. 1996 , publisher=","work_id":"b5f9da6e-46d3-4a8c-b140-334bb90f1e84","year":1996},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Inverting estimating equations for causal inference on quantiles , author=. Biometrika , volume=. 2025 , publisher=","work_id":"c7326007-98e1-440e-b552-a7b955bc902b","year":2025}],"snapshot_sha256":"9bbf07b1708de9c9885cae07f38a01a7b85206101e02410bd093a61b9625d8bf"},"source":{"id":"2605.14275","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T02:35:42.644855Z","id":"8f76fbb7-58ae-489b-ab07-2aa5cf1d9064","model_set":{"reader":"grok-4.3"},"one_line_summary":"A doubly robust estimator is developed for quantile treatment effects on long-term outcomes by integrating randomized trial data with observational data under surrogate transportability, remaining consistent if either nuisance function is correctly estimated.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A doubly robust estimator identifies quantile treatment effects on long-term outcomes by linking trial surrogates to external data.","strongest_claim":"Under treatment randomization, positivity, and a surrogate-based transportability assumption, we establish identification and develop a doubly robust estimator for inference. The estimator accommodates flexible machine learning methods for nuisance estimation, remains consistent if either the score-related or outcome regression-related nuisance functions are consistently estimated, and is asymptotically normal under regularity conditions.","weakest_assumption":"The surrogate-based transportability assumption that permits linking short-term surrogates observed in the randomized trial to long-term outcomes in the external observational data."}},"verdict_id":"8f76fbb7-58ae-489b-ab07-2aa5cf1d9064"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:528d27e6346390345b785bfd3325f1e6b1ecd8879cafa52a0373fca010527cb9","target":"record","created_at":"2026-05-17T23:39:10Z","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":"ee19ff875ac76f6263b008403213cc73533f7cc1faa8116e2efb47d72658e6f3","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-05-14T02:19:41Z","title_canon_sha256":"2b69707950ae304da74973cd88d48a2cd1dbdf667e2916317ad0636400470530"},"schema_version":"1.0","source":{"id":"2605.14275","kind":"arxiv","version":1}},"canonical_sha256":"8762c4b10579992e9b18a02aec6f121d6f4a2c53b9eec700869346f62acfa33a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8762c4b10579992e9b18a02aec6f121d6f4a2c53b9eec700869346f62acfa33a","first_computed_at":"2026-05-17T23:39:10.350635Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:10.350635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dVLtHMLtcOYjQYHxvcWsoRxFwugKueiYUP3WH94eKIjXussphnWRKCvU0USKpPATcnNrGK5ES1aP6J829CC2DA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:10.351168Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14275","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:528d27e6346390345b785bfd3325f1e6b1ecd8879cafa52a0373fca010527cb9","sha256:a676bcf855795f8310a9122327475e6b20367d1da349f4e431d82133d9b36d4f"],"state_sha256":"f1c4b1c1669258facce016cf0df368d9b5523b7a49c91a126802861d8dcefc68"}