{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MIICRXAIDHWYVCW2RIR6XRV6L7","short_pith_number":"pith:MIICRXAI","schema_version":"1.0","canonical_sha256":"621028dc0819ed8a8ada8a23ebc6be5fdc072337967bb2a4292938a800f5af12","source":{"kind":"arxiv","id":"2409.13190","version":2},"attestation_state":"computed","paper":{"title":"Nonparametric Causal Survival Analysis with Clustered Interference","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chanhwa Lee, Donglin Zeng, John D. Clemens, Michael Emch, Michael G. Hudgens","submitted_at":"2024-09-20T03:43:48Z","abstract_excerpt":"Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of other units, i.e., there is interference. In some settings, units may be grouped into clusters such that it is reasonable to assume interference only occurs within clusters, i.e., there is clustered interference. In this paper, methods are developed which can accommodate confounding, censored outcomes, and clustered interference. The approach avoids"},"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":"2409.13190","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-09-20T03:43:48Z","cross_cats_sorted":[],"title_canon_sha256":"3935e6dc0b281ba368f9692106533f468e296fdc3ac6daf39d56862c655fffda","abstract_canon_sha256":"451ba2a7631816d83363e62eef0160b3d73e15ad94816d70d2b7d1b7e711e1e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:14.935287Z","signature_b64":"w/pmKls/kdZk/nm6z7H6n/WSuJhHvcqn7NJcLdCDiFwa3sibzgCH5bfksjMifXH0u98Pg0YgWubsM2xLTFXaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"621028dc0819ed8a8ada8a23ebc6be5fdc072337967bb2a4292938a800f5af12","last_reissued_at":"2026-07-05T11:52:14.934846Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:14.934846Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Causal Survival Analysis with Clustered Interference","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Chanhwa Lee, Donglin Zeng, John D. Clemens, Michael Emch, Michael G. Hudgens","submitted_at":"2024-09-20T03:43:48Z","abstract_excerpt":"Inferring treatment effects on a survival time outcome based on data from an observational study is challenging due to the presence of censoring and possible confounding. An additional challenge occurs when a unit's treatment affects the outcome of other units, i.e., there is interference. In some settings, units may be grouped into clusters such that it is reasonable to assume interference only occurs within clusters, i.e., there is clustered interference. In this paper, methods are developed which can accommodate confounding, censored outcomes, and clustered interference. The approach avoids"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.13190","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/2409.13190/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":"2409.13190","created_at":"2026-07-05T11:52:14.934902+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.13190v2","created_at":"2026-07-05T11:52:14.934902+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.13190","created_at":"2026-07-05T11:52:14.934902+00:00"},{"alias_kind":"pith_short_12","alias_value":"MIICRXAIDHWY","created_at":"2026-07-05T11:52:14.934902+00:00"},{"alias_kind":"pith_short_16","alias_value":"MIICRXAIDHWYVCW2","created_at":"2026-07-05T11:52:14.934902+00:00"},{"alias_kind":"pith_short_8","alias_value":"MIICRXAI","created_at":"2026-07-05T11:52:14.934902+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.13008","citing_title":"Nonparametric efficient inference for network quantile causal effects under partial interference","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7","json":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7.json","graph_json":"https://pith.science/api/pith-number/MIICRXAIDHWYVCW2RIR6XRV6L7/graph.json","events_json":"https://pith.science/api/pith-number/MIICRXAIDHWYVCW2RIR6XRV6L7/events.json","paper":"https://pith.science/paper/MIICRXAI"},"agent_actions":{"view_html":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7","download_json":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7.json","view_paper":"https://pith.science/paper/MIICRXAI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.13190&json=true","fetch_graph":"https://pith.science/api/pith-number/MIICRXAIDHWYVCW2RIR6XRV6L7/graph.json","fetch_events":"https://pith.science/api/pith-number/MIICRXAIDHWYVCW2RIR6XRV6L7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7/action/storage_attestation","attest_author":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7/action/author_attestation","sign_citation":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7/action/citation_signature","submit_replication":"https://pith.science/pith/MIICRXAIDHWYVCW2RIR6XRV6L7/action/replication_record"}},"created_at":"2026-07-05T11:52:14.934902+00:00","updated_at":"2026-07-05T11:52:14.934902+00:00"}