{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:WHUCQ3YP4EXLMPLN5DWMS7LH6J","short_pith_number":"pith:WHUCQ3YP","schema_version":"1.0","canonical_sha256":"b1e8286f0fe12eb63d6de8ecc97d67f24ba992e7ced1b0058a72cd4582987da5","source":{"kind":"arxiv","id":"2305.04116","version":2},"attestation_state":"computed","paper":{"title":"The Fundamental Limits of Structure-Agnostic Functional Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Edward H. Kennedy, Larry Wasserman, Sivaraman Balakrishnan","submitted_at":"2023-05-06T18:40:04Z","abstract_excerpt":"Many recent developments in causal inference, and functional estimation problems more generally, have been motivated by the fact that classical one-step (first-order) debiasing methods, or their more recent sample-split double machine-learning avatars, can outperform plugin estimators under surprisingly weak conditions. These first-order corrections improve on plugin estimators in a black-box fashion, and consequently are often used in conjunction with powerful off-the-shelf estimation methods. These first-order methods are however provably suboptimal in a minimax sense for functional estimati"},"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":"2305.04116","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-05-06T18:40:04Z","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"e72e9a1920a68e8375cab9a4807f0d155296f7028bc4dc32b70a6241bc0b9ee1","abstract_canon_sha256":"917f5d31f36992abe37c8e40fbcf70e9d85b558ab912133eac04b848c63f5176"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:30.477727Z","signature_b64":"JDdxbLMaMgRW1G+Vjy4zGlnd0OJ6VkDL5IaFewbcojB5Yf/DxQuwpfinYo1fU3EDC9bEzfR/FFM7Ri5PBQa6BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1e8286f0fe12eb63d6de8ecc97d67f24ba992e7ced1b0058a72cd4582987da5","last_reissued_at":"2026-07-05T11:17:30.477237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:30.477237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Fundamental Limits of Structure-Agnostic Functional Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Edward H. Kennedy, Larry Wasserman, Sivaraman Balakrishnan","submitted_at":"2023-05-06T18:40:04Z","abstract_excerpt":"Many recent developments in causal inference, and functional estimation problems more generally, have been motivated by the fact that classical one-step (first-order) debiasing methods, or their more recent sample-split double machine-learning avatars, can outperform plugin estimators under surprisingly weak conditions. These first-order corrections improve on plugin estimators in a black-box fashion, and consequently are often used in conjunction with powerful off-the-shelf estimation methods. These first-order methods are however provably suboptimal in a minimax sense for functional estimati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.04116","kind":"arxiv","version":2},"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/2305.04116/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":"2305.04116","created_at":"2026-07-05T11:17:30.477299+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.04116v2","created_at":"2026-07-05T11:17:30.477299+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.04116","created_at":"2026-07-05T11:17:30.477299+00:00"},{"alias_kind":"pith_short_12","alias_value":"WHUCQ3YP4EXL","created_at":"2026-07-05T11:17:30.477299+00:00"},{"alias_kind":"pith_short_16","alias_value":"WHUCQ3YP4EXLMPLN","created_at":"2026-07-05T11:17:30.477299+00:00"},{"alias_kind":"pith_short_8","alias_value":"WHUCQ3YP","created_at":"2026-07-05T11:17:30.477299+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06368","citing_title":"Optimally taming biases in black-box models for efficient semiparametric estimation","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2502.04274","citing_title":"Orthogonal Representation Learning for Estimating Causal Quantities","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J","json":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J.json","graph_json":"https://pith.science/api/pith-number/WHUCQ3YP4EXLMPLN5DWMS7LH6J/graph.json","events_json":"https://pith.science/api/pith-number/WHUCQ3YP4EXLMPLN5DWMS7LH6J/events.json","paper":"https://pith.science/paper/WHUCQ3YP"},"agent_actions":{"view_html":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J","download_json":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J.json","view_paper":"https://pith.science/paper/WHUCQ3YP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.04116&json=true","fetch_graph":"https://pith.science/api/pith-number/WHUCQ3YP4EXLMPLN5DWMS7LH6J/graph.json","fetch_events":"https://pith.science/api/pith-number/WHUCQ3YP4EXLMPLN5DWMS7LH6J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/action/storage_attestation","attest_author":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/action/author_attestation","sign_citation":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/action/citation_signature","submit_replication":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/action/replication_record"}},"created_at":"2026-07-05T11:17:30.477299+00:00","updated_at":"2026-07-05T11:17:30.477299+00:00"}