{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4GTKEJFRRZAR6ODLBESTZHQAIE","short_pith_number":"pith:4GTKEJFR","schema_version":"1.0","canonical_sha256":"e1a6a224b18e411f386b09253c9e0041253092b59ddd1476618e7e2225d0e9c2","source":{"kind":"arxiv","id":"2505.20787","version":1},"attestation_state":"computed","paper":{"title":"Debiased Ill-Posed Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["econ.EM","stat.ML"],"primary_cat":"stat.ME","authors_text":"AmirEmad Ghassami, Andrea Rotnitzky, James M. Robins","submitted_at":"2025-05-27T06:47:33Z","abstract_excerpt":"In various statistical settings, the goal is to estimate a function which is restricted by the statistical model only through a conditional moment restriction. Prominent examples include the nonparametric instrumental variable framework for estimating the structural function of the outcome variable, and the proximal causal inference framework for estimating the bridge functions. A common strategy in the literature is to find the minimizer of the projected mean squared error. However, this approach can be sensitive to misspecification or slow convergence rate of the estimators of the involved n"},"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":"2505.20787","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2025-05-27T06:47:33Z","cross_cats_sorted":["econ.EM","stat.ML"],"title_canon_sha256":"d0ad730c7e92b3173c281dcf50d89cd8e3b8fe27caef558c230b0111da2f429c","abstract_canon_sha256":"0d20d4f46b03e25a1c45ff74badc9cd60dbaab7969bd1e33b8d8fcfdc0c2d5e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:18.888017Z","signature_b64":"Byht4SGbe+o7pwmwRqZ+auI0WaYUkF0Og/NkbzJnahIeuZOZU0tN8DsZ0Zoy8E/WC5GPHXP7XcWvd8BW6BCqBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1a6a224b18e411f386b09253c9e0041253092b59ddd1476618e7e2225d0e9c2","last_reissued_at":"2026-07-05T11:10:18.887522Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:18.887522Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Debiased Ill-Posed Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["econ.EM","stat.ML"],"primary_cat":"stat.ME","authors_text":"AmirEmad Ghassami, Andrea Rotnitzky, James M. Robins","submitted_at":"2025-05-27T06:47:33Z","abstract_excerpt":"In various statistical settings, the goal is to estimate a function which is restricted by the statistical model only through a conditional moment restriction. Prominent examples include the nonparametric instrumental variable framework for estimating the structural function of the outcome variable, and the proximal causal inference framework for estimating the bridge functions. A common strategy in the literature is to find the minimizer of the projected mean squared error. However, this approach can be sensitive to misspecification or slow convergence rate of the estimators of the involved n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.20787","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/2505.20787/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":"2505.20787","created_at":"2026-07-05T11:10:18.887582+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.20787v1","created_at":"2026-07-05T11:10:18.887582+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.20787","created_at":"2026-07-05T11:10:18.887582+00:00"},{"alias_kind":"pith_short_12","alias_value":"4GTKEJFRRZAR","created_at":"2026-07-05T11:10:18.887582+00:00"},{"alias_kind":"pith_short_16","alias_value":"4GTKEJFRRZAR6ODL","created_at":"2026-07-05T11:10:18.887582+00:00"},{"alias_kind":"pith_short_8","alias_value":"4GTKEJFR","created_at":"2026-07-05T11:10:18.887582+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17232","citing_title":"Semiparametric Mediation Analysis with Separately Observed Mediator and Outcome under Unmeasured Confounding","ref_index":124,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE","json":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE.json","graph_json":"https://pith.science/api/pith-number/4GTKEJFRRZAR6ODLBESTZHQAIE/graph.json","events_json":"https://pith.science/api/pith-number/4GTKEJFRRZAR6ODLBESTZHQAIE/events.json","paper":"https://pith.science/paper/4GTKEJFR"},"agent_actions":{"view_html":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE","download_json":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE.json","view_paper":"https://pith.science/paper/4GTKEJFR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.20787&json=true","fetch_graph":"https://pith.science/api/pith-number/4GTKEJFRRZAR6ODLBESTZHQAIE/graph.json","fetch_events":"https://pith.science/api/pith-number/4GTKEJFRRZAR6ODLBESTZHQAIE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE/action/storage_attestation","attest_author":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE/action/author_attestation","sign_citation":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE/action/citation_signature","submit_replication":"https://pith.science/pith/4GTKEJFRRZAR6ODLBESTZHQAIE/action/replication_record"}},"created_at":"2026-07-05T11:10:18.887582+00:00","updated_at":"2026-07-05T11:10:18.887582+00:00"}