{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:53VKJYTMS5P6MPXPCZYITTEXPB","short_pith_number":"pith:53VKJYTM","schema_version":"1.0","canonical_sha256":"eeeaa4e26c975fe63eef167089cc97787c130ca4e8646245350b9bbc70f2f9b0","source":{"kind":"arxiv","id":"2412.08869","version":1},"attestation_state":"computed","paper":{"title":"Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.AP","authors_text":"Dominik Rothenh\\\"ausler, Naoki Egami, Ying Jin","submitted_at":"2024-12-12T02:06:27Z","abstract_excerpt":"Many existing approaches to generalizing statistical inference amidst distribution shift operate under the covariate shift assumption, which posits that the conditional distribution of unobserved variables given observable ones is invariant across populations. However, recent empirical investigations have demonstrated that adjusting for shift in observed variables (covariate shift) is often insufficient for generalization. In other words, covariate shift does not typically ``explain away'' the distribution shift between settings. As such, addressing the unknown yet non-negligible shift in the "},"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":"2412.08869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2024-12-12T02:06:27Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"35bb6355573030bab829397ab78ffc60799a0143445059dd9d50a06e1425a212","abstract_canon_sha256":"b07034a39630c2b3a38aeb127645e349985abc270d9758cfc915324639f7fc5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:48:03.269321Z","signature_b64":"8izWxUZDh3f1ayB9EoQCVuX2Wy8fG9KE27Ze+Mip2WHw7hlHvE5/XI+00VNUJI18bpGVAsFDe6ofNgzcZ1QiBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eeeaa4e26c975fe63eef167089cc97787c130ca4e8646245350b9bbc70f2f9b0","last_reissued_at":"2026-07-05T09:48:03.268902Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:48:03.268902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Reweighting: On the Predictive Role of Covariate Shift in Effect Generalization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.AP","authors_text":"Dominik Rothenh\\\"ausler, Naoki Egami, Ying Jin","submitted_at":"2024-12-12T02:06:27Z","abstract_excerpt":"Many existing approaches to generalizing statistical inference amidst distribution shift operate under the covariate shift assumption, which posits that the conditional distribution of unobserved variables given observable ones is invariant across populations. However, recent empirical investigations have demonstrated that adjusting for shift in observed variables (covariate shift) is often insufficient for generalization. In other words, covariate shift does not typically ``explain away'' the distribution shift between settings. As such, addressing the unknown yet non-negligible shift in the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08869","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/2412.08869/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":"2412.08869","created_at":"2026-07-05T09:48:03.268953+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.08869v1","created_at":"2026-07-05T09:48:03.268953+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08869","created_at":"2026-07-05T09:48:03.268953+00:00"},{"alias_kind":"pith_short_12","alias_value":"53VKJYTMS5P6","created_at":"2026-07-05T09:48:03.268953+00:00"},{"alias_kind":"pith_short_16","alias_value":"53VKJYTMS5P6MPXP","created_at":"2026-07-05T09:48:03.268953+00:00"},{"alias_kind":"pith_short_8","alias_value":"53VKJYTM","created_at":"2026-07-05T09:48:03.268953+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.13287","citing_title":"Optimal Empirical Risk Minimization under Temporal Distribution Shifts","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB","json":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB.json","graph_json":"https://pith.science/api/pith-number/53VKJYTMS5P6MPXPCZYITTEXPB/graph.json","events_json":"https://pith.science/api/pith-number/53VKJYTMS5P6MPXPCZYITTEXPB/events.json","paper":"https://pith.science/paper/53VKJYTM"},"agent_actions":{"view_html":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB","download_json":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB.json","view_paper":"https://pith.science/paper/53VKJYTM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.08869&json=true","fetch_graph":"https://pith.science/api/pith-number/53VKJYTMS5P6MPXPCZYITTEXPB/graph.json","fetch_events":"https://pith.science/api/pith-number/53VKJYTMS5P6MPXPCZYITTEXPB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB/action/storage_attestation","attest_author":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB/action/author_attestation","sign_citation":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB/action/citation_signature","submit_replication":"https://pith.science/pith/53VKJYTMS5P6MPXPCZYITTEXPB/action/replication_record"}},"created_at":"2026-07-05T09:48:03.268953+00:00","updated_at":"2026-07-05T09:48:03.268953+00:00"}