{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:D64USFODVRTKQKCOVSK34KLPNS","short_pith_number":"pith:D64USFOD","schema_version":"1.0","canonical_sha256":"1fb94915c3ac66a8284eac95be296f6ca77d1bfb1545446ecde352dab427c06a","source":{"kind":"arxiv","id":"2606.06440","version":1},"attestation_state":"computed","paper":{"title":"Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Greg van Anders, Hazhir Aliahmadi, Irina Babayan","submitted_at":"2026-06-04T17:41:32Z","abstract_excerpt":"Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science. Bayesian networks offer a probabilistic method for modelling generic causal relationships via directed acyclic graphs (DAGs). However, typical techniques for constructing Bayesian networks rely on optimization, which can be ill-suited for learning causal relationships because the underlying data may admit multiple chains of causation. More data-faithful representations of causal relationships would provide frameworks for constructing multiple causal maps tha"},"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":"2606.06440","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-04T17:41:32Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"b2415e8e86e8383b412658b501c96c02bc607a43573bd0d89f0620e336889a52","abstract_canon_sha256":"8fddedbb17427ff2556f8e19ab3e775b93b8f004351e1367fb106fcba8da6363"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:45.623052Z","signature_b64":"3gZ7/XxSHL2oMBQua9D+N+3kQEXi35y3bFKMK2P/uOwZ/mzeAbjjFAem0AV5yrSiXsdLn0uBV2BnOazGoR6jDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fb94915c3ac66a8284eac95be296f6ca77d1bfb1545446ecde352dab427c06a","last_reissued_at":"2026-06-05T01:15:45.622591Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:45.622591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Greg van Anders, Hazhir Aliahmadi, Irina Babayan","submitted_at":"2026-06-04T17:41:32Z","abstract_excerpt":"Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science. Bayesian networks offer a probabilistic method for modelling generic causal relationships via directed acyclic graphs (DAGs). However, typical techniques for constructing Bayesian networks rely on optimization, which can be ill-suited for learning causal relationships because the underlying data may admit multiple chains of causation. More data-faithful representations of causal relationships would provide frameworks for constructing multiple causal maps tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06440","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/2606.06440/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":"2606.06440","created_at":"2026-06-05T01:15:45.622648+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06440v1","created_at":"2026-06-05T01:15:45.622648+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06440","created_at":"2026-06-05T01:15:45.622648+00:00"},{"alias_kind":"pith_short_12","alias_value":"D64USFODVRTK","created_at":"2026-06-05T01:15:45.622648+00:00"},{"alias_kind":"pith_short_16","alias_value":"D64USFODVRTKQKCO","created_at":"2026-06-05T01:15:45.622648+00:00"},{"alias_kind":"pith_short_8","alias_value":"D64USFOD","created_at":"2026-06-05T01:15:45.622648+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/D64USFODVRTKQKCOVSK34KLPNS","json":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS.json","graph_json":"https://pith.science/api/pith-number/D64USFODVRTKQKCOVSK34KLPNS/graph.json","events_json":"https://pith.science/api/pith-number/D64USFODVRTKQKCOVSK34KLPNS/events.json","paper":"https://pith.science/paper/D64USFOD"},"agent_actions":{"view_html":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS","download_json":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS.json","view_paper":"https://pith.science/paper/D64USFOD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06440&json=true","fetch_graph":"https://pith.science/api/pith-number/D64USFODVRTKQKCOVSK34KLPNS/graph.json","fetch_events":"https://pith.science/api/pith-number/D64USFODVRTKQKCOVSK34KLPNS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS/action/storage_attestation","attest_author":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS/action/author_attestation","sign_citation":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS/action/citation_signature","submit_replication":"https://pith.science/pith/D64USFODVRTKQKCOVSK34KLPNS/action/replication_record"}},"created_at":"2026-06-05T01:15:45.622648+00:00","updated_at":"2026-06-05T01:15:45.622648+00:00"}