{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:3ZOV4SVBB6CCWLDUL3XAGCW7XE","short_pith_number":"pith:3ZOV4SVB","schema_version":"1.0","canonical_sha256":"de5d5e4aa10f842b2c745eee030adfb92c22e46824511fc6209a1fe29204558d","source":{"kind":"arxiv","id":"2006.07433","version":3},"attestation_state":"computed","paper":{"title":"A causal framework for distribution generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jonas Peters, Martin Emil Jakobsen, Nicola Gnecco, Niklas Pfister, Rune Christiansen","submitted_at":"2020-06-12T19:24:02Z","abstract_excerpt":"We consider the problem of predicting a response $Y$ from a set of covariates $X$ when test and training distributions differ. Since such differences may have causal explanations, we consider test distributions that emerge from interventions in a structural causal model, and focus on minimizing the worst-case risk. Causal regression models, which regress the response on its direct causes, remain unchanged under arbitrary interventions on the covariates, but they are not always optimal in the above sense. For example, for linear models and bounded interventions, alternative solutions have been "},"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":"2006.07433","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2020-06-12T19:24:02Z","cross_cats_sorted":[],"title_canon_sha256":"4397c384e2726e2a02c1e4d20f301e3afc1cb2dea728a743bb5b0af0e35589ee","abstract_canon_sha256":"d3e595c4b5f66d628c010c999b8031ef40a9c4ab9d8bb88b84afbc84706a595c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:06:43.944965Z","signature_b64":"raNUe5PxTKYDbneswBPQg4DdEEUG7LmYJyHqQvjeEA3OTZeJBGb9uMyqI5X1jm7fTdFhhsDTryHZ2/ZcnWjsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de5d5e4aa10f842b2c745eee030adfb92c22e46824511fc6209a1fe29204558d","last_reissued_at":"2026-07-05T03:06:43.944569Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:06:43.944569Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A causal framework for distribution generalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jonas Peters, Martin Emil Jakobsen, Nicola Gnecco, Niklas Pfister, Rune Christiansen","submitted_at":"2020-06-12T19:24:02Z","abstract_excerpt":"We consider the problem of predicting a response $Y$ from a set of covariates $X$ when test and training distributions differ. Since such differences may have causal explanations, we consider test distributions that emerge from interventions in a structural causal model, and focus on minimizing the worst-case risk. Causal regression models, which regress the response on its direct causes, remain unchanged under arbitrary interventions on the covariates, but they are not always optimal in the above sense. For example, for linear models and bounded interventions, alternative solutions have been "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.07433","kind":"arxiv","version":3},"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/2006.07433/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":"2006.07433","created_at":"2026-07-05T03:06:43.944624+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.07433v3","created_at":"2026-07-05T03:06:43.944624+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.07433","created_at":"2026-07-05T03:06:43.944624+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ZOV4SVBB6CC","created_at":"2026-07-05T03:06:43.944624+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ZOV4SVBB6CCWLDU","created_at":"2026-07-05T03:06:43.944624+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ZOV4SVB","created_at":"2026-07-05T03:06:43.944624+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.02109","citing_title":"Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE","json":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE.json","graph_json":"https://pith.science/api/pith-number/3ZOV4SVBB6CCWLDUL3XAGCW7XE/graph.json","events_json":"https://pith.science/api/pith-number/3ZOV4SVBB6CCWLDUL3XAGCW7XE/events.json","paper":"https://pith.science/paper/3ZOV4SVB"},"agent_actions":{"view_html":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE","download_json":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE.json","view_paper":"https://pith.science/paper/3ZOV4SVB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.07433&json=true","fetch_graph":"https://pith.science/api/pith-number/3ZOV4SVBB6CCWLDUL3XAGCW7XE/graph.json","fetch_events":"https://pith.science/api/pith-number/3ZOV4SVBB6CCWLDUL3XAGCW7XE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE/action/storage_attestation","attest_author":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE/action/author_attestation","sign_citation":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE/action/citation_signature","submit_replication":"https://pith.science/pith/3ZOV4SVBB6CCWLDUL3XAGCW7XE/action/replication_record"}},"created_at":"2026-07-05T03:06:43.944624+00:00","updated_at":"2026-07-05T03:06:43.944624+00:00"}