{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PFAEKRSGT3HV4OXRO34F7IFNMN","short_pith_number":"pith:PFAEKRSG","schema_version":"1.0","canonical_sha256":"79404546469ecf5e3af176f85fa0ad636bd593d233cac06c5be885c5321aaf25","source":{"kind":"arxiv","id":"2401.12369","version":1},"attestation_state":"computed","paper":{"title":"SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"cs.LG","authors_text":"Lang Li, Ping Zhang, Ruoqi Liu, Seungyeon Lee, Wenyu Song","submitted_at":"2024-01-22T21:41:26Z","abstract_excerpt":"Precise estimation of treatment effects is crucial for evaluating intervention effectiveness. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they treat the entire population as a homogeneous group, overlooking the diversity of treatment effects across potential subgroups that have varying treatment effects. This limitation restricts the ability to precisely estimate treatment effects and provide subgroup-specific treatment recommendations. In thi"},"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":"2401.12369","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-01-22T21:41:26Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"e553aad34e69a3721a40ac895ed26fc3eaa8a285660522cda33bfda3a7ff2c85","abstract_canon_sha256":"9531cfb1a2af720d1e113c7d4bcf5721d7d18a6a1cd6b20e7673482785e64180"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:29.493352Z","signature_b64":"/EKlOMwpQTIksrKB5VkQuzuPUY57sr3KKEQQLvQTTy4hVN0HGMhHaq//0fr3tY/DFVBZRtHOeIKAhHEhlszFBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79404546469ecf5e3af176f85fa0ad636bd593d233cac06c5be885c5321aaf25","last_reissued_at":"2026-07-05T07:36:29.493008Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:29.493008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"cs.LG","authors_text":"Lang Li, Ping Zhang, Ruoqi Liu, Seungyeon Lee, Wenyu Song","submitted_at":"2024-01-22T21:41:26Z","abstract_excerpt":"Precise estimation of treatment effects is crucial for evaluating intervention effectiveness. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they treat the entire population as a homogeneous group, overlooking the diversity of treatment effects across potential subgroups that have varying treatment effects. This limitation restricts the ability to precisely estimate treatment effects and provide subgroup-specific treatment recommendations. In thi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.12369","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/2401.12369/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":"2401.12369","created_at":"2026-07-05T07:36:29.493062+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.12369v1","created_at":"2026-07-05T07:36:29.493062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.12369","created_at":"2026-07-05T07:36:29.493062+00:00"},{"alias_kind":"pith_short_12","alias_value":"PFAEKRSGT3HV","created_at":"2026-07-05T07:36:29.493062+00:00"},{"alias_kind":"pith_short_16","alias_value":"PFAEKRSGT3HV4OXR","created_at":"2026-07-05T07:36:29.493062+00:00"},{"alias_kind":"pith_short_8","alias_value":"PFAEKRSG","created_at":"2026-07-05T07:36:29.493062+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/PFAEKRSGT3HV4OXRO34F7IFNMN","json":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN.json","graph_json":"https://pith.science/api/pith-number/PFAEKRSGT3HV4OXRO34F7IFNMN/graph.json","events_json":"https://pith.science/api/pith-number/PFAEKRSGT3HV4OXRO34F7IFNMN/events.json","paper":"https://pith.science/paper/PFAEKRSG"},"agent_actions":{"view_html":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN","download_json":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN.json","view_paper":"https://pith.science/paper/PFAEKRSG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.12369&json=true","fetch_graph":"https://pith.science/api/pith-number/PFAEKRSGT3HV4OXRO34F7IFNMN/graph.json","fetch_events":"https://pith.science/api/pith-number/PFAEKRSGT3HV4OXRO34F7IFNMN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN/action/storage_attestation","attest_author":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN/action/author_attestation","sign_citation":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN/action/citation_signature","submit_replication":"https://pith.science/pith/PFAEKRSGT3HV4OXRO34F7IFNMN/action/replication_record"}},"created_at":"2026-07-05T07:36:29.493062+00:00","updated_at":"2026-07-05T07:36:29.493062+00:00"}