{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WSCLQXJCNDJSGBRTL46XPSXCZV","short_pith_number":"pith:WSCLQXJC","schema_version":"1.0","canonical_sha256":"b484b85d2268d32306335f3d77cae2cd6d3f1cc2d1d7fe4175aaa492d68e3882","source":{"kind":"arxiv","id":"1810.02894","version":1},"attestation_state":"computed","paper":{"title":"Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Angela Zhou, Nathan Kallus, Xiaojie Mao","submitted_at":"2018-10-05T21:42:40Z","abstract_excerpt":"We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to individual causal effect predictions and is key for personalized assessments. Recent work has focused on how to learn CATE under unconfoundedness, i.e., when there are no unobserved confounders. Since CATE may not be identified when unconfoundedness is violated, we develop a functional interval estimator that predicts bounds on the individual causal effects under realistic violations of unconfoundedness. Our estimator t"},"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":"1810.02894","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-10-05T21:42:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b6f126c2961e272f601c4e56812d46fb97ed912c75dce157ff3483f9ba30c23b","abstract_canon_sha256":"fa2cf139407613578a4f6af82b81c1fa60482bab071b6194cd10dd8e50b66405"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:59.949814Z","signature_b64":"o4zWT7w9duq3MF5gMp64RE7pyQZoem+dLb2rRV7WAD+0FSTf4Z37S+MVx2WbQOPce1jupgVvI7Llj127/se2Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b484b85d2268d32306335f3d77cae2cd6d3f1cc2d1d7fe4175aaa492d68e3882","last_reissued_at":"2026-05-18T00:03:59.949177Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:59.949177Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Angela Zhou, Nathan Kallus, Xiaojie Mao","submitted_at":"2018-10-05T21:42:40Z","abstract_excerpt":"We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to individual causal effect predictions and is key for personalized assessments. Recent work has focused on how to learn CATE under unconfoundedness, i.e., when there are no unobserved confounders. Since CATE may not be identified when unconfoundedness is violated, we develop a functional interval estimator that predicts bounds on the individual causal effects under realistic violations of unconfoundedness. Our estimator t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.02894","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":"1810.02894","created_at":"2026-05-18T00:03:59.949289+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.02894v1","created_at":"2026-05-18T00:03:59.949289+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.02894","created_at":"2026-05-18T00:03:59.949289+00:00"},{"alias_kind":"pith_short_12","alias_value":"WSCLQXJCNDJS","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"WSCLQXJCNDJSGBRT","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"WSCLQXJC","created_at":"2026-05-18T12:33:01.666342+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/WSCLQXJCNDJSGBRTL46XPSXCZV","json":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV.json","graph_json":"https://pith.science/api/pith-number/WSCLQXJCNDJSGBRTL46XPSXCZV/graph.json","events_json":"https://pith.science/api/pith-number/WSCLQXJCNDJSGBRTL46XPSXCZV/events.json","paper":"https://pith.science/paper/WSCLQXJC"},"agent_actions":{"view_html":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV","download_json":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV.json","view_paper":"https://pith.science/paper/WSCLQXJC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.02894&json=true","fetch_graph":"https://pith.science/api/pith-number/WSCLQXJCNDJSGBRTL46XPSXCZV/graph.json","fetch_events":"https://pith.science/api/pith-number/WSCLQXJCNDJSGBRTL46XPSXCZV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV/action/storage_attestation","attest_author":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV/action/author_attestation","sign_citation":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV/action/citation_signature","submit_replication":"https://pith.science/pith/WSCLQXJCNDJSGBRTL46XPSXCZV/action/replication_record"}},"created_at":"2026-05-18T00:03:59.949289+00:00","updated_at":"2026-05-18T00:03:59.949289+00:00"}