{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:J4FUTA2EUPZRSQU6KQNET4ZFKG","short_pith_number":"pith:J4FUTA2E","schema_version":"1.0","canonical_sha256":"4f0b498344a3f319429e541a49f325519bd9d9be2be71ba6f13f16bdc5a4cb9f","source":{"kind":"arxiv","id":"2001.03998","version":2},"attestation_state":"computed","paper":{"title":"Towards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Elias Chaibub Neto","submitted_at":"2020-01-12T20:49:07Z","abstract_excerpt":"We propose a counterfactual approach to train ``causality-aware\" predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., prediction tasks where the outcome influences the features). In applications plagued by confounding, the approach can be used to generate predictions that are free from the influence of observed confounders. In applications involving observed mediators, the approach can be used to generate predictions that only capture the direct or the indirect causal influences. Mechanistically, we train supervised learners on (coun"},"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":"2001.03998","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-01-12T20:49:07Z","cross_cats_sorted":[],"title_canon_sha256":"1f7c39883d29c27a9b81e255669246747707dacb8e220887916a6559e58fc344","abstract_canon_sha256":"c421b8c882a056363d4d2c9ee7a04ab38a0b0108ae7383e9190de305d8014c52"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:07.893386Z","signature_b64":"v9GCRaN5QeyKkeG+73jQOiP1mdpYV/pPF8lJM4UGiYeYdFvHwAtQpWb0nTN1Q1VaK/kFPQ01OUma49t7Ru8FBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f0b498344a3f319429e541a49f325519bd9d9be2be71ba6f13f16bdc5a4cb9f","last_reissued_at":"2026-07-05T01:55:07.892897Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:07.892897Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Elias Chaibub Neto","submitted_at":"2020-01-12T20:49:07Z","abstract_excerpt":"We propose a counterfactual approach to train ``causality-aware\" predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., prediction tasks where the outcome influences the features). In applications plagued by confounding, the approach can be used to generate predictions that are free from the influence of observed confounders. In applications involving observed mediators, the approach can be used to generate predictions that only capture the direct or the indirect causal influences. Mechanistically, we train supervised learners on (coun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.03998","kind":"arxiv","version":2},"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/2001.03998/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":"2001.03998","created_at":"2026-07-05T01:55:07.892951+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.03998v2","created_at":"2026-07-05T01:55:07.892951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.03998","created_at":"2026-07-05T01:55:07.892951+00:00"},{"alias_kind":"pith_short_12","alias_value":"J4FUTA2EUPZR","created_at":"2026-07-05T01:55:07.892951+00:00"},{"alias_kind":"pith_short_16","alias_value":"J4FUTA2EUPZRSQU6","created_at":"2026-07-05T01:55:07.892951+00:00"},{"alias_kind":"pith_short_8","alias_value":"J4FUTA2E","created_at":"2026-07-05T01:55:07.892951+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.04275","citing_title":"Scoping review of methodology for aiding generalisability and transportability of clinical prediction models","ref_index":50,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG","json":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG.json","graph_json":"https://pith.science/api/pith-number/J4FUTA2EUPZRSQU6KQNET4ZFKG/graph.json","events_json":"https://pith.science/api/pith-number/J4FUTA2EUPZRSQU6KQNET4ZFKG/events.json","paper":"https://pith.science/paper/J4FUTA2E"},"agent_actions":{"view_html":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG","download_json":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG.json","view_paper":"https://pith.science/paper/J4FUTA2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.03998&json=true","fetch_graph":"https://pith.science/api/pith-number/J4FUTA2EUPZRSQU6KQNET4ZFKG/graph.json","fetch_events":"https://pith.science/api/pith-number/J4FUTA2EUPZRSQU6KQNET4ZFKG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG/action/storage_attestation","attest_author":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG/action/author_attestation","sign_citation":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG/action/citation_signature","submit_replication":"https://pith.science/pith/J4FUTA2EUPZRSQU6KQNET4ZFKG/action/replication_record"}},"created_at":"2026-07-05T01:55:07.892951+00:00","updated_at":"2026-07-05T01:55:07.892951+00:00"}