{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GPNOYEZUNKA5WHPID4CSZGZP2S","short_pith_number":"pith:GPNOYEZU","schema_version":"1.0","canonical_sha256":"33daec13346a81db1de81f052c9b2fd483dba5f0eaf4bd62618d081106ba2fef","source":{"kind":"arxiv","id":"2312.02717","version":1},"attestation_state":"computed","paper":{"title":"A Graphical Approach to Treatment Effect Estimation with Observational Network Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Leonard Henckel, Marloes H. Maathuis, Meta-Lina Spohn","submitted_at":"2023-12-05T12:29:09Z","abstract_excerpt":"We propose an easy-to-use adjustment estimator for the effect of a treatment based on observational data from a single (social) network of units. The approach allows for interactions among units within the network, called interference, and for observed confounding. We define a simplified causal graph that does not differentiate between units, called generic graph. Using valid adjustment sets determined in the generic graph, we can identify the treatment effect and build a corresponding estimator. We establish the estimator's consistency and its convergence to a Gaussian limiting distribution a"},"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":"2312.02717","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2023-12-05T12:29:09Z","cross_cats_sorted":[],"title_canon_sha256":"500345e270eb3d32cba7b1f005059f44183cce79fcd2217b214f68d4e1b8b255","abstract_canon_sha256":"a6999a570f1c12b80902a26246ffb06b01a7bf01fb49e9ce2dbb22e6ac3b75c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:22.077357Z","signature_b64":"IdFRKHFvDP/jrwhtAATb8xOQZVQozsVNvO7aPCnMz4PopQKbM7ENu83i4jqKjI/voRdcMUBe929fpwbF8idiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33daec13346a81db1de81f052c9b2fd483dba5f0eaf4bd62618d081106ba2fef","last_reissued_at":"2026-07-05T07:20:22.076878Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:22.076878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Graphical Approach to Treatment Effect Estimation with Observational Network Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Leonard Henckel, Marloes H. Maathuis, Meta-Lina Spohn","submitted_at":"2023-12-05T12:29:09Z","abstract_excerpt":"We propose an easy-to-use adjustment estimator for the effect of a treatment based on observational data from a single (social) network of units. The approach allows for interactions among units within the network, called interference, and for observed confounding. We define a simplified causal graph that does not differentiate between units, called generic graph. Using valid adjustment sets determined in the generic graph, we can identify the treatment effect and build a corresponding estimator. We establish the estimator's consistency and its convergence to a Gaussian limiting distribution a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02717","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/2312.02717/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":"2312.02717","created_at":"2026-07-05T07:20:22.076937+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.02717v1","created_at":"2026-07-05T07:20:22.076937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02717","created_at":"2026-07-05T07:20:22.076937+00:00"},{"alias_kind":"pith_short_12","alias_value":"GPNOYEZUNKA5","created_at":"2026-07-05T07:20:22.076937+00:00"},{"alias_kind":"pith_short_16","alias_value":"GPNOYEZUNKA5WHPI","created_at":"2026-07-05T07:20:22.076937+00:00"},{"alias_kind":"pith_short_8","alias_value":"GPNOYEZU","created_at":"2026-07-05T07:20:22.076937+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/GPNOYEZUNKA5WHPID4CSZGZP2S","json":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S.json","graph_json":"https://pith.science/api/pith-number/GPNOYEZUNKA5WHPID4CSZGZP2S/graph.json","events_json":"https://pith.science/api/pith-number/GPNOYEZUNKA5WHPID4CSZGZP2S/events.json","paper":"https://pith.science/paper/GPNOYEZU"},"agent_actions":{"view_html":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S","download_json":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S.json","view_paper":"https://pith.science/paper/GPNOYEZU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.02717&json=true","fetch_graph":"https://pith.science/api/pith-number/GPNOYEZUNKA5WHPID4CSZGZP2S/graph.json","fetch_events":"https://pith.science/api/pith-number/GPNOYEZUNKA5WHPID4CSZGZP2S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S/action/storage_attestation","attest_author":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S/action/author_attestation","sign_citation":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S/action/citation_signature","submit_replication":"https://pith.science/pith/GPNOYEZUNKA5WHPID4CSZGZP2S/action/replication_record"}},"created_at":"2026-07-05T07:20:22.076937+00:00","updated_at":"2026-07-05T07:20:22.076937+00:00"}