{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:WHUCQ3YP4EXLMPLN5DWMS7LH6J","short_pith_number":"pith:WHUCQ3YP","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"},"canonical_sha256":"b1e8286f0fe12eb63d6de8ecc97d67f24ba992e7ced1b0058a72cd4582987da5","source":{"kind":"arxiv","id":"2305.04116","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.04116","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"arxiv_version","alias_value":"2305.04116v2","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.04116","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"pith_short_12","alias_value":"WHUCQ3YP4EXL","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"pith_short_16","alias_value":"WHUCQ3YP4EXLMPLN","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"pith_short_8","alias_value":"WHUCQ3YP","created_at":"2026-07-05T11:17:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:WHUCQ3YP4EXLMPLN5DWMS7LH6J","target":"record","payload":{"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"},"canonical_sha256":"b1e8286f0fe12eb63d6de8ecc97d67f24ba992e7ced1b0058a72cd4582987da5","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"},"source_kind":"arxiv","source_id":"2305.04116","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rraH3TM44dmZd2dZgexbdXVTWMVbxFbjJC/9VXxhC1acO8TZn/fN3eSb9cluWomBVSddgbKMaDcLWydJi9F+Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T20:03:53.997654Z"},"content_sha256":"4dfecf0949b2116bc2c51c913b0156b3011082f9435c5547750d3e0a5e8a3966","schema_version":"1.0","event_id":"sha256:4dfecf0949b2116bc2c51c913b0156b3011082f9435c5547750d3e0a5e8a3966"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:WHUCQ3YP4EXLMPLN5DWMS7LH6J","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FSpLNdEkvhyQgmMjXViQjhdy4w/MyyR1fE8uJkUCCkdHU0qGUXJH6O3g1AOjvzXMA5DxJL1AS+lO/OGwNWr7Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T20:03:53.998759Z"},"content_sha256":"54ba8daae43cc520f55348e3ac0979865f1e7b6bbc8de8fd29fabefda1e0ad9e","schema_version":"1.0","event_id":"sha256:54ba8daae43cc520f55348e3ac0979865f1e7b6bbc8de8fd29fabefda1e0ad9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/bundle.json","state_url":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T20:03:54Z","links":{"resolver":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J","bundle":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/bundle.json","state":"https://pith.science/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WHUCQ3YP4EXLMPLN5DWMS7LH6J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:WHUCQ3YP4EXLMPLN5DWMS7LH6J","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":"917f5d31f36992abe37c8e40fbcf70e9d85b558ab912133eac04b848c63f5176","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-05-06T18:40:04Z","title_canon_sha256":"e72e9a1920a68e8375cab9a4807f0d155296f7028bc4dc32b70a6241bc0b9ee1"},"schema_version":"1.0","source":{"id":"2305.04116","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.04116","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"arxiv_version","alias_value":"2305.04116v2","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.04116","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"pith_short_12","alias_value":"WHUCQ3YP4EXL","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"pith_short_16","alias_value":"WHUCQ3YP4EXLMPLN","created_at":"2026-07-05T11:17:30Z"},{"alias_kind":"pith_short_8","alias_value":"WHUCQ3YP","created_at":"2026-07-05T11:17:30Z"}],"graph_snapshots":[{"event_id":"sha256:54ba8daae43cc520f55348e3ac0979865f1e7b6bbc8de8fd29fabefda1e0ad9e","target":"graph","created_at":"2026-07-05T11:17:30Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2305.04116/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Edward H. Kennedy, Larry Wasserman, Sivaraman Balakrishnan","cross_cats":["stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-05-06T18:40:04Z","title":"The Fundamental Limits of Structure-Agnostic Functional Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.04116","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4dfecf0949b2116bc2c51c913b0156b3011082f9435c5547750d3e0a5e8a3966","target":"record","created_at":"2026-07-05T11:17:30Z","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":"917f5d31f36992abe37c8e40fbcf70e9d85b558ab912133eac04b848c63f5176","cross_cats_sorted":["stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2023-05-06T18:40:04Z","title_canon_sha256":"e72e9a1920a68e8375cab9a4807f0d155296f7028bc4dc32b70a6241bc0b9ee1"},"schema_version":"1.0","source":{"id":"2305.04116","kind":"arxiv","version":2}},"canonical_sha256":"b1e8286f0fe12eb63d6de8ecc97d67f24ba992e7ced1b0058a72cd4582987da5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b1e8286f0fe12eb63d6de8ecc97d67f24ba992e7ced1b0058a72cd4582987da5","first_computed_at":"2026-07-05T11:17:30.477237Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:30.477237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JDdxbLMaMgRW1G+Vjy4zGlnd0OJ6VkDL5IaFewbcojB5Yf/DxQuwpfinYo1fU3EDC9bEzfR/FFM7Ri5PBQa6BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:30.477727Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.04116","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4dfecf0949b2116bc2c51c913b0156b3011082f9435c5547750d3e0a5e8a3966","sha256:54ba8daae43cc520f55348e3ac0979865f1e7b6bbc8de8fd29fabefda1e0ad9e"],"state_sha256":"edc30ac7042263807643e72c25d594f14f97722e2ab7e771de08a56e464e10e5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3HS1qlC3rMLEEW6ZZ7fB+8/ApzU+oerObjT11kXpNVGZE9VsanVtjKSmULeMonXn2mcBqLrqG89bZGlBQBPfCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T20:03:54.007670Z","bundle_sha256":"12b4f413168efb4186572fdcaba4bd5e7bd6e0a127e431d0997ad0ff355f28a4"}}