{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:7BKYIWVZWNJPIBEU2VJZSCUUGA","short_pith_number":"pith:7BKYIWVZ","schema_version":"1.0","canonical_sha256":"f855845ab9b352f40494d553990a943027b63ea5b4b2ab2ad2676534ffc02e24","source":{"kind":"arxiv","id":"1709.04240","version":2},"attestation_state":"computed","paper":{"title":"A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bryan Andrews, Joseph D. Ramsey","submitted_at":"2017-09-13T10:41:19Z","abstract_excerpt":"We compare Tetrad (Java) algorithms to the other public software packages BNT (Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \\vanilla\" task of recovering DAG structure to the extent possible from data generated recursively from linear, Gaussian structure equation models (SEMs) with no latent variables, for random graphs, with no additional knowledge of variable order or adjacency structure, and without additional specification of intervention information. Each one of the above packages offers at least one implementation suitable to this purpose. We compare them on adjacency and ori"},"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":"1709.04240","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-09-13T10:41:19Z","cross_cats_sorted":[],"title_canon_sha256":"a40052b6c6db5b472d43e0255a4b3ee289e87e2c72604c12aca04c7991828605","abstract_canon_sha256":"79fd1121e2e7a87e61114f6f54852fa1e391c2e625bac811d77eef1f4b8bf0da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:03.387737Z","signature_b64":"1Embhd4JAJCjOaKJUV6kYQQyD9sB5sAx0Re6/56BR9eyHaUCW2TmM8ipmM3p8QQAU0XzZo5BHq6veS7MxYcNCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f855845ab9b352f40494d553990a943027b63ea5b4b2ab2ad2676534ffc02e24","last_reissued_at":"2026-05-18T00:35:03.387021Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:03.387021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comparison of Public Causal Search Packages on Linear, Gaussian Data with No Latent Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bryan Andrews, Joseph D. Ramsey","submitted_at":"2017-09-13T10:41:19Z","abstract_excerpt":"We compare Tetrad (Java) algorithms to the other public software packages BNT (Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \\vanilla\" task of recovering DAG structure to the extent possible from data generated recursively from linear, Gaussian structure equation models (SEMs) with no latent variables, for random graphs, with no additional knowledge of variable order or adjacency structure, and without additional specification of intervention information. Each one of the above packages offers at least one implementation suitable to this purpose. We compare them on adjacency and ori"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.04240","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":""},"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":"1709.04240","created_at":"2026-05-18T00:35:03.387149+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.04240v2","created_at":"2026-05-18T00:35:03.387149+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.04240","created_at":"2026-05-18T00:35:03.387149+00:00"},{"alias_kind":"pith_short_12","alias_value":"7BKYIWVZWNJP","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"7BKYIWVZWNJPIBEU","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"7BKYIWVZ","created_at":"2026-05-18T12:31:03.183658+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/7BKYIWVZWNJPIBEU2VJZSCUUGA","json":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA.json","graph_json":"https://pith.science/api/pith-number/7BKYIWVZWNJPIBEU2VJZSCUUGA/graph.json","events_json":"https://pith.science/api/pith-number/7BKYIWVZWNJPIBEU2VJZSCUUGA/events.json","paper":"https://pith.science/paper/7BKYIWVZ"},"agent_actions":{"view_html":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA","download_json":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA.json","view_paper":"https://pith.science/paper/7BKYIWVZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.04240&json=true","fetch_graph":"https://pith.science/api/pith-number/7BKYIWVZWNJPIBEU2VJZSCUUGA/graph.json","fetch_events":"https://pith.science/api/pith-number/7BKYIWVZWNJPIBEU2VJZSCUUGA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA/action/storage_attestation","attest_author":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA/action/author_attestation","sign_citation":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA/action/citation_signature","submit_replication":"https://pith.science/pith/7BKYIWVZWNJPIBEU2VJZSCUUGA/action/replication_record"}},"created_at":"2026-05-18T00:35:03.387149+00:00","updated_at":"2026-05-18T00:35:03.387149+00:00"}