{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:OWRFOQ3X6DERXVG7FZKAIB7U5D","short_pith_number":"pith:OWRFOQ3X","schema_version":"1.0","canonical_sha256":"75a2574377f0c91bd4df2e540407f4e8f5ad54e13bcf584422bf95bc484f51fa","source":{"kind":"arxiv","id":"2109.10522","version":1},"attestation_state":"computed","paper":{"title":"Minimax Rates and Adaptivity in Combining Experimental and Observational Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Bo Zhang, Shuxiao Chen, Ting Ye","submitted_at":"2021-09-22T05:31:00Z","abstract_excerpt":"Randomized controlled trials (RCTs) are the gold standard for evaluating the causal effect of a treatment; however, they often have limited sample sizes and sometimes poor generalizability. On the other hand, non-randomized, observational data derived from large administrative databases have massive sample sizes and better generalizability, but they are prone to unmeasured confounding bias. It is thus of considerable interest to reconcile effect estimates obtained from randomized controlled trials and observational studies investigating the same intervention, potentially harvesting the best fr"},"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":"2109.10522","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2021-09-22T05:31:00Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"74d80f0650915cc38330893ce2317e562dc8d1b17d828d7f3277faa54ffdfbe5","abstract_canon_sha256":"519b01d6579f842e7d94da788394a992f4ac3dff085d23304fb1184a51044950"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:16:37.286172Z","signature_b64":"BOxdRd2UB+29olIj4EdLoDg1X6pWvCUZmvjFEJQwGteOQVmt9jL6+5NSD7KCH2F2QxtfbFtbg8hhTMXItDRiAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75a2574377f0c91bd4df2e540407f4e8f5ad54e13bcf584422bf95bc484f51fa","last_reissued_at":"2026-07-05T03:16:37.285768Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:16:37.285768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Minimax Rates and Adaptivity in Combining Experimental and Observational Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Bo Zhang, Shuxiao Chen, Ting Ye","submitted_at":"2021-09-22T05:31:00Z","abstract_excerpt":"Randomized controlled trials (RCTs) are the gold standard for evaluating the causal effect of a treatment; however, they often have limited sample sizes and sometimes poor generalizability. On the other hand, non-randomized, observational data derived from large administrative databases have massive sample sizes and better generalizability, but they are prone to unmeasured confounding bias. It is thus of considerable interest to reconcile effect estimates obtained from randomized controlled trials and observational studies investigating the same intervention, potentially harvesting the best fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.10522","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2109.10522/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2109.10522","created_at":"2026-07-05T03:16:37.285828+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.10522v1","created_at":"2026-07-05T03:16:37.285828+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.10522","created_at":"2026-07-05T03:16:37.285828+00:00"},{"alias_kind":"pith_short_12","alias_value":"OWRFOQ3X6DER","created_at":"2026-07-05T03:16:37.285828+00:00"},{"alias_kind":"pith_short_16","alias_value":"OWRFOQ3X6DERXVG7","created_at":"2026-07-05T03:16:37.285828+00:00"},{"alias_kind":"pith_short_8","alias_value":"OWRFOQ3X","created_at":"2026-07-05T03:16:37.285828+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20427","citing_title":"Private Rate-Double-Robust Inference","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30615","citing_title":"Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2410.02941","citing_title":"Efficient collaborative learning of the average treatment effect","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D","json":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D.json","graph_json":"https://pith.science/api/pith-number/OWRFOQ3X6DERXVG7FZKAIB7U5D/graph.json","events_json":"https://pith.science/api/pith-number/OWRFOQ3X6DERXVG7FZKAIB7U5D/events.json","paper":"https://pith.science/paper/OWRFOQ3X"},"agent_actions":{"view_html":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D","download_json":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D.json","view_paper":"https://pith.science/paper/OWRFOQ3X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.10522&json=true","fetch_graph":"https://pith.science/api/pith-number/OWRFOQ3X6DERXVG7FZKAIB7U5D/graph.json","fetch_events":"https://pith.science/api/pith-number/OWRFOQ3X6DERXVG7FZKAIB7U5D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D/action/storage_attestation","attest_author":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D/action/author_attestation","sign_citation":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D/action/citation_signature","submit_replication":"https://pith.science/pith/OWRFOQ3X6DERXVG7FZKAIB7U5D/action/replication_record"}},"created_at":"2026-07-05T03:16:37.285828+00:00","updated_at":"2026-07-05T03:16:37.285828+00:00"}