{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:G4WSMIU7SZ4PW7TRO2NFAPUELE","short_pith_number":"pith:G4WSMIU7","schema_version":"1.0","canonical_sha256":"372d26229f9678fb7e71769a503e845938fa640e031051a64a5ae870fef73731","source":{"kind":"arxiv","id":"2405.15887","version":2},"attestation_state":"computed","paper":{"title":"Data-adaptive exposure thresholds for the Horvitz-Thompson estimator of the Average Treatment Effect in experiments with network interference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jennifer Brennan, Tyler McCormick, Vydhourie Thiyageswaran","submitted_at":"2024-05-24T19:10:35Z","abstract_excerpt":"Randomized controlled trials often suffer from interference, a violation of the Stable Unit Treatment Values Assumption (SUTVA) in which a unit's treatment assignment affects the outcomes of its neighbors. This interference causes bias in naive estimators of the average treatment effect (ATE). A popular method to achieve unbiasedness is to pair the Horvitz-Thompson estimator of the ATE with a known exposure mapping: a function that identifies which units in a given randomization are not subject to interference. For example, an exposure mapping can specify that any unit with at least $h$-fracti"},"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":"2405.15887","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-05-24T19:10:35Z","cross_cats_sorted":[],"title_canon_sha256":"cced13450e0cff447db24773c92ea446dff6b180f360383d4216c1db5d6fb497","abstract_canon_sha256":"50d1ab72456230718429b2dcb942249cd5337d28aff32a635b2fae18cb15e828"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:44.223814Z","signature_b64":"WvbWnWWsBkOHd3TgbaAsUoiapCSlStTTyKXQBNR7yJyXjRQBqy4G14xkzO0oFm5gpaURVoMUGg769WAoCucaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"372d26229f9678fb7e71769a503e845938fa640e031051a64a5ae870fef73731","last_reissued_at":"2026-07-05T10:09:44.223395Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:44.223395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-adaptive exposure thresholds for the Horvitz-Thompson estimator of the Average Treatment Effect in experiments with network interference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jennifer Brennan, Tyler McCormick, Vydhourie Thiyageswaran","submitted_at":"2024-05-24T19:10:35Z","abstract_excerpt":"Randomized controlled trials often suffer from interference, a violation of the Stable Unit Treatment Values Assumption (SUTVA) in which a unit's treatment assignment affects the outcomes of its neighbors. This interference causes bias in naive estimators of the average treatment effect (ATE). A popular method to achieve unbiasedness is to pair the Horvitz-Thompson estimator of the ATE with a known exposure mapping: a function that identifies which units in a given randomization are not subject to interference. For example, an exposure mapping can specify that any unit with at least $h$-fracti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15887","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/2405.15887/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":"2405.15887","created_at":"2026-07-05T10:09:44.223453+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.15887v2","created_at":"2026-07-05T10:09:44.223453+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15887","created_at":"2026-07-05T10:09:44.223453+00:00"},{"alias_kind":"pith_short_12","alias_value":"G4WSMIU7SZ4P","created_at":"2026-07-05T10:09:44.223453+00:00"},{"alias_kind":"pith_short_16","alias_value":"G4WSMIU7SZ4PW7TR","created_at":"2026-07-05T10:09:44.223453+00:00"},{"alias_kind":"pith_short_8","alias_value":"G4WSMIU7","created_at":"2026-07-05T10:09:44.223453+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/G4WSMIU7SZ4PW7TRO2NFAPUELE","json":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE.json","graph_json":"https://pith.science/api/pith-number/G4WSMIU7SZ4PW7TRO2NFAPUELE/graph.json","events_json":"https://pith.science/api/pith-number/G4WSMIU7SZ4PW7TRO2NFAPUELE/events.json","paper":"https://pith.science/paper/G4WSMIU7"},"agent_actions":{"view_html":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE","download_json":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE.json","view_paper":"https://pith.science/paper/G4WSMIU7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.15887&json=true","fetch_graph":"https://pith.science/api/pith-number/G4WSMIU7SZ4PW7TRO2NFAPUELE/graph.json","fetch_events":"https://pith.science/api/pith-number/G4WSMIU7SZ4PW7TRO2NFAPUELE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE/action/storage_attestation","attest_author":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE/action/author_attestation","sign_citation":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE/action/citation_signature","submit_replication":"https://pith.science/pith/G4WSMIU7SZ4PW7TRO2NFAPUELE/action/replication_record"}},"created_at":"2026-07-05T10:09:44.223453+00:00","updated_at":"2026-07-05T10:09:44.223453+00:00"}