{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:DLDWQSA3OSLBZOLUY2XEE2S7QN","short_pith_number":"pith:DLDWQSA3","schema_version":"1.0","canonical_sha256":"1ac768481b74961cb974c6ae426a5f837d2c7cf7c41ecae786ac30d2e713d805","source":{"kind":"arxiv","id":"1808.04904","version":1},"attestation_state":"computed","paper":{"title":"False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Nanyu Chen, Xiaolin Shi, Yuxiang Xie","submitted_at":"2018-08-14T21:40:17Z","abstract_excerpt":"Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose stati"},"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":"1808.04904","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2018-08-14T21:40:17Z","cross_cats_sorted":[],"title_canon_sha256":"35264b2ecb457aabd6ee6366a2e4d351f82e0bc464fc0f99885765ccb0e1e78a","abstract_canon_sha256":"78081a5117240306b869b5024d3155c583b341e3ae83ea8d964ab771025bc436"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:02.259899Z","signature_b64":"YMp3OnuKdHa7XhVNd6FCPl2aNTt4n06WOHxes6fBUZFYEM381oU14f6TSEGeVetEl5SAOu/P0CvB4ePwOYm3Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ac768481b74961cb974c6ae426a5f837d2c7cf7c41ecae786ac30d2e713d805","last_reissued_at":"2026-05-18T00:08:02.259322Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:02.259322Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Nanyu Chen, Xiaolin Shi, Yuxiang Xie","submitted_at":"2018-08-14T21:40:17Z","abstract_excerpt":"Online controlled experiments (a.k.a. A/B testing) have been used as the mantra for data-driven decision making on feature changing and product shipping in many Internet companies. However, it is still a great challenge to systematically measure how every code or feature change impacts millions of users with great heterogeneity (e.g. countries, ages, devices). The most commonly used A/B testing framework in many companies is based on Average Treatment Effect (ATE), which cannot detect the heterogeneity of treatment effect on users with different characteristics. In this paper, we propose stati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04904","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":""},"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":"1808.04904","created_at":"2026-05-18T00:08:02.259413+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.04904v1","created_at":"2026-05-18T00:08:02.259413+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04904","created_at":"2026-05-18T00:08:02.259413+00:00"},{"alias_kind":"pith_short_12","alias_value":"DLDWQSA3OSLB","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"DLDWQSA3OSLBZOLU","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"DLDWQSA3","created_at":"2026-05-18T12:32:19.392346+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/DLDWQSA3OSLBZOLUY2XEE2S7QN","json":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN.json","graph_json":"https://pith.science/api/pith-number/DLDWQSA3OSLBZOLUY2XEE2S7QN/graph.json","events_json":"https://pith.science/api/pith-number/DLDWQSA3OSLBZOLUY2XEE2S7QN/events.json","paper":"https://pith.science/paper/DLDWQSA3"},"agent_actions":{"view_html":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN","download_json":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN.json","view_paper":"https://pith.science/paper/DLDWQSA3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.04904&json=true","fetch_graph":"https://pith.science/api/pith-number/DLDWQSA3OSLBZOLUY2XEE2S7QN/graph.json","fetch_events":"https://pith.science/api/pith-number/DLDWQSA3OSLBZOLUY2XEE2S7QN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN/action/storage_attestation","attest_author":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN/action/author_attestation","sign_citation":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN/action/citation_signature","submit_replication":"https://pith.science/pith/DLDWQSA3OSLBZOLUY2XEE2S7QN/action/replication_record"}},"created_at":"2026-05-18T00:08:02.259413+00:00","updated_at":"2026-05-18T00:08:02.259413+00:00"}