{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5RTXYI2L2CCYRMUWCDPH5T7VSZ","short_pith_number":"pith:5RTXYI2L","schema_version":"1.0","canonical_sha256":"ec677c234bd08588b29610de7ecff5964e3b3a0bcafadc93418f377ac759d89f","source":{"kind":"arxiv","id":"2501.05844","version":3},"attestation_state":"computed","paper":{"title":"\"Cause\" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of \"Causal Machine Learning\"","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gustav Sir, Leonardo Christov Moore, Martin Krutsky, Vyacheslav Kungurtsev","submitted_at":"2025-01-10T10:36:26Z","abstract_excerpt":"Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising specific computational techniques to apply to datasets that reveal the true nature of cause and effect in a number of important domains. In this paper we consider the epistemology of recognizing true cause and effect phenomena. We apply the Ordinary Language method of engaging on the customary use of the word 'cause' to investigate valid semantics of reasoning about cause and effect. We recognize that the grammars of cause and effect are fundamentally distinct in form across s"},"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":"2501.05844","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-10T10:36:26Z","cross_cats_sorted":[],"title_canon_sha256":"94eace771bc069335e2aee7dad309082fd9fea5f0b1f3d068a7e9c26f9efdce2","abstract_canon_sha256":"d3b5db5bdf1e8a2dbbf47380c38d99eb34e16e97316382012a4dd6c5e45a9d4f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:52.425468Z","signature_b64":"zwGkOCcLjS8ePyoJm7/liA9174MudlIp+QlYe+k6wnLcl1yuS+hZVp4z/oD8JX8jtUQOdQbjxaULGWMB2Vk8AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec677c234bd08588b29610de7ecff5964e3b3a0bcafadc93418f377ac759d89f","last_reissued_at":"2026-07-05T11:14:52.424988Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:52.424988Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"\"Cause\" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of \"Causal Machine Learning\"","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gustav Sir, Leonardo Christov Moore, Martin Krutsky, Vyacheslav Kungurtsev","submitted_at":"2025-01-10T10:36:26Z","abstract_excerpt":"Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising specific computational techniques to apply to datasets that reveal the true nature of cause and effect in a number of important domains. In this paper we consider the epistemology of recognizing true cause and effect phenomena. We apply the Ordinary Language method of engaging on the customary use of the word 'cause' to investigate valid semantics of reasoning about cause and effect. We recognize that the grammars of cause and effect are fundamentally distinct in form across s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.05844","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/2501.05844/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":"2501.05844","created_at":"2026-07-05T11:14:52.425041+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.05844v3","created_at":"2026-07-05T11:14:52.425041+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.05844","created_at":"2026-07-05T11:14:52.425041+00:00"},{"alias_kind":"pith_short_12","alias_value":"5RTXYI2L2CCY","created_at":"2026-07-05T11:14:52.425041+00:00"},{"alias_kind":"pith_short_16","alias_value":"5RTXYI2L2CCYRMUW","created_at":"2026-07-05T11:14:52.425041+00:00"},{"alias_kind":"pith_short_8","alias_value":"5RTXYI2L","created_at":"2026-07-05T11:14:52.425041+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.05844","citing_title":"\"Cause\" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of \"Causal Machine Learning\"","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ","json":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ.json","graph_json":"https://pith.science/api/pith-number/5RTXYI2L2CCYRMUWCDPH5T7VSZ/graph.json","events_json":"https://pith.science/api/pith-number/5RTXYI2L2CCYRMUWCDPH5T7VSZ/events.json","paper":"https://pith.science/paper/5RTXYI2L"},"agent_actions":{"view_html":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ","download_json":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ.json","view_paper":"https://pith.science/paper/5RTXYI2L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.05844&json=true","fetch_graph":"https://pith.science/api/pith-number/5RTXYI2L2CCYRMUWCDPH5T7VSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/5RTXYI2L2CCYRMUWCDPH5T7VSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ/action/storage_attestation","attest_author":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ/action/author_attestation","sign_citation":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ/action/citation_signature","submit_replication":"https://pith.science/pith/5RTXYI2L2CCYRMUWCDPH5T7VSZ/action/replication_record"}},"created_at":"2026-07-05T11:14:52.425041+00:00","updated_at":"2026-07-05T11:14:52.425041+00:00"}