{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:27FPS2PXGHEHSWCLJMCL6S57WM","short_pith_number":"pith:27FPS2PX","schema_version":"1.0","canonical_sha256":"d7caf969f731c879584b4b04bf4bbfb3240169f672e87ccf987a625313137a09","source":{"kind":"arxiv","id":"1804.03105","version":2},"attestation_state":"computed","paper":{"title":"Central limit theorems via Stein's method for randomized experiments under interference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Alex Chin","submitted_at":"2018-04-09T17:11:37Z","abstract_excerpt":"We study conditions under which treatment effect estimators constructed under the no-interference assumption in randomized experiments are asymptotically normal in the presence of interference. We prove that the standard Horvitz-Thompson estimator is asymptotically normal under a restricted interference condition characterized by limiting the degree of the dependency graph. The amount of interference is allowed to grow with the population size. We then provide a central limit theorem for the difference-in-means estimator that can handle interference that exists between all pairs of units, prov"},"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":"1804.03105","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-04-09T17:11:37Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"756ae2483f26968c0605af165d9f4b90bcfe4430be771a04063b8adf4fbc1686","abstract_canon_sha256":"6e1495061bc1c1077f9cfdd69eaf2a77144fcafb93dafeb35557c2d896d892bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:54.642542Z","signature_b64":"RauN47lpq0J4Zh3mO1AIHDebU6n9flAIKF1m3BI9p7t5N7MSk+CsMJz4+CVfMuxTx6OeBHhk5mg8F7ZON2CLCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7caf969f731c879584b4b04bf4bbfb3240169f672e87ccf987a625313137a09","last_reissued_at":"2026-05-17T23:51:54.642060Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:54.642060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Central limit theorems via Stein's method for randomized experiments under interference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Alex Chin","submitted_at":"2018-04-09T17:11:37Z","abstract_excerpt":"We study conditions under which treatment effect estimators constructed under the no-interference assumption in randomized experiments are asymptotically normal in the presence of interference. We prove that the standard Horvitz-Thompson estimator is asymptotically normal under a restricted interference condition characterized by limiting the degree of the dependency graph. The amount of interference is allowed to grow with the population size. We then provide a central limit theorem for the difference-in-means estimator that can handle interference that exists between all pairs of units, prov"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.03105","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":""},"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":"1804.03105","created_at":"2026-05-17T23:51:54.642133+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.03105v2","created_at":"2026-05-17T23:51:54.642133+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.03105","created_at":"2026-05-17T23:51:54.642133+00:00"},{"alias_kind":"pith_short_12","alias_value":"27FPS2PXGHEH","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"27FPS2PXGHEHSWCL","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"27FPS2PX","created_at":"2026-05-18T12:31:59.375834+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.06008","citing_title":"Causal Inference under Interference: Regression Adjustment and Optimality","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM","json":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM.json","graph_json":"https://pith.science/api/pith-number/27FPS2PXGHEHSWCLJMCL6S57WM/graph.json","events_json":"https://pith.science/api/pith-number/27FPS2PXGHEHSWCLJMCL6S57WM/events.json","paper":"https://pith.science/paper/27FPS2PX"},"agent_actions":{"view_html":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM","download_json":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM.json","view_paper":"https://pith.science/paper/27FPS2PX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.03105&json=true","fetch_graph":"https://pith.science/api/pith-number/27FPS2PXGHEHSWCLJMCL6S57WM/graph.json","fetch_events":"https://pith.science/api/pith-number/27FPS2PXGHEHSWCLJMCL6S57WM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM/action/storage_attestation","attest_author":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM/action/author_attestation","sign_citation":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM/action/citation_signature","submit_replication":"https://pith.science/pith/27FPS2PXGHEHSWCLJMCL6S57WM/action/replication_record"}},"created_at":"2026-05-17T23:51:54.642133+00:00","updated_at":"2026-05-17T23:51:54.642133+00:00"}