{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2KG3L35SAGU4MK6CBWAOPOEQDI","short_pith_number":"pith:2KG3L35S","schema_version":"1.0","canonical_sha256":"d28db5efb201a9c62bc20d80e7b8901a108fa9ff47f7a2caaace5de0d75a43d7","source":{"kind":"arxiv","id":"2602.12604","version":2},"attestation_state":"computed","paper":{"title":"Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rule","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Guanhua Chen, Joowon Lee","submitted_at":"2026-02-13T04:22:46Z","abstract_excerpt":"Differential privacy provides a formal framework for releasing statistical estimators that limit how much any single observation can influence the output, by injecting calibrated random noise. We study differentially private estimation in two-stage procedures common in causal inference and individualized treatment rule (ITR) learning, in which data-dependent weights are first estimated to enforce covariate balance and a parameter of interest is then obtained by weighted empirical risk minimization. We propose Differentially Private Two-Stage Empirical Risk Minimization (DP-2ERM), which privati"},"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":"2602.12604","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2026-02-13T04:22:46Z","cross_cats_sorted":["stat.ML","stat.TH"],"title_canon_sha256":"b05833a8c2abcedccaf9e5c064c75c49c32f0b96a784ed7247f2f60cf3d93a54","abstract_canon_sha256":"4fef61e89bee20a99c2ad5b03d38d6535975e7b813e0853f04fa7953df6a55f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:25.398550Z","signature_b64":"yOsvME1ojQiADgXWTJj3DgX7x7hk4uDWLMHI9H59ur6pbXTsUTJff9gq3saboJqAiNPQwFhaQRdO+enYNgN9AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d28db5efb201a9c62bc20d80e7b8901a108fa9ff47f7a2caaace5de0d75a43d7","last_reissued_at":"2026-05-26T01:03:25.397595Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:25.397595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rule","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Guanhua Chen, Joowon Lee","submitted_at":"2026-02-13T04:22:46Z","abstract_excerpt":"Differential privacy provides a formal framework for releasing statistical estimators that limit how much any single observation can influence the output, by injecting calibrated random noise. We study differentially private estimation in two-stage procedures common in causal inference and individualized treatment rule (ITR) learning, in which data-dependent weights are first estimated to enforce covariate balance and a parameter of interest is then obtained by weighted empirical risk minimization. We propose Differentially Private Two-Stage Empirical Risk Minimization (DP-2ERM), which privati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.12604","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/2602.12604/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":"2602.12604","created_at":"2026-05-26T01:03:25.397752+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.12604v2","created_at":"2026-05-26T01:03:25.397752+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.12604","created_at":"2026-05-26T01:03:25.397752+00:00"},{"alias_kind":"pith_short_12","alias_value":"2KG3L35SAGU4","created_at":"2026-05-26T01:03:25.397752+00:00"},{"alias_kind":"pith_short_16","alias_value":"2KG3L35SAGU4MK6C","created_at":"2026-05-26T01:03:25.397752+00:00"},{"alias_kind":"pith_short_8","alias_value":"2KG3L35S","created_at":"2026-05-26T01:03:25.397752+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI","json":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI.json","graph_json":"https://pith.science/api/pith-number/2KG3L35SAGU4MK6CBWAOPOEQDI/graph.json","events_json":"https://pith.science/api/pith-number/2KG3L35SAGU4MK6CBWAOPOEQDI/events.json","paper":"https://pith.science/paper/2KG3L35S"},"agent_actions":{"view_html":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI","download_json":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI.json","view_paper":"https://pith.science/paper/2KG3L35S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.12604&json=true","fetch_graph":"https://pith.science/api/pith-number/2KG3L35SAGU4MK6CBWAOPOEQDI/graph.json","fetch_events":"https://pith.science/api/pith-number/2KG3L35SAGU4MK6CBWAOPOEQDI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI/action/storage_attestation","attest_author":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI/action/author_attestation","sign_citation":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI/action/citation_signature","submit_replication":"https://pith.science/pith/2KG3L35SAGU4MK6CBWAOPOEQDI/action/replication_record"}},"created_at":"2026-05-26T01:03:25.397752+00:00","updated_at":"2026-05-26T01:03:25.397752+00:00"}