{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:4EWPGX7DL4JJTG2WYAMTGUDEM2","short_pith_number":"pith:4EWPGX7D","canonical_record":{"source":{"id":"1309.6843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-09-26T12:43:12Z","cross_cats_sorted":[],"title_canon_sha256":"918ff613c9ebb998b3ff27e95dfae7fbe3674d0c37cce4d563a76e0fcb012f7b","abstract_canon_sha256":"65c4959dbdb9e4a97256ad1b0012d79a70c58b828538f451d6c1d0b5c8662266"},"schema_version":"1.0"},"canonical_sha256":"e12cf35fe35f12999b56c019335064669ea18248ca618429ca0dd71f72ccef4d","source":{"kind":"arxiv","id":"1309.6843","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1309.6843","created_at":"2026-05-18T03:12:10Z"},{"alias_kind":"arxiv_version","alias_value":"1309.6843v1","created_at":"2026-05-18T03:12:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.6843","created_at":"2026-05-18T03:12:10Z"},{"alias_kind":"pith_short_12","alias_value":"4EWPGX7DL4JJ","created_at":"2026-05-18T12:27:32Z"},{"alias_kind":"pith_short_16","alias_value":"4EWPGX7DL4JJTG2W","created_at":"2026-05-18T12:27:32Z"},{"alias_kind":"pith_short_8","alias_value":"4EWPGX7D","created_at":"2026-05-18T12:27:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:4EWPGX7DL4JJTG2WYAMTGUDEM2","target":"record","payload":{"canonical_record":{"source":{"id":"1309.6843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-09-26T12:43:12Z","cross_cats_sorted":[],"title_canon_sha256":"918ff613c9ebb998b3ff27e95dfae7fbe3674d0c37cce4d563a76e0fcb012f7b","abstract_canon_sha256":"65c4959dbdb9e4a97256ad1b0012d79a70c58b828538f451d6c1d0b5c8662266"},"schema_version":"1.0"},"canonical_sha256":"e12cf35fe35f12999b56c019335064669ea18248ca618429ca0dd71f72ccef4d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:12:10.147534Z","signature_b64":"Y0RsdbSXcSSHmEOidR/NPmGH4S/gt9VqQR2Y2gK2pfS/kaWd4824J61HWM8aKgwbdIlUq3f7g0b8VZqqKNNzBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e12cf35fe35f12999b56c019335064669ea18248ca618429ca0dd71f72ccef4d","last_reissued_at":"2026-05-18T03:12:10.146860Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:12:10.146860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1309.6843","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:12:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6HpSO9XNG72JSAlRehqOvogkXfXUAtTUGZsyObEP7xzkmQU+W6iIaVequ6RgqrZl27dPFaIaxDngSC9WiNK+Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T11:20:41.061503Z"},"content_sha256":"4507615a99b8f4f9bf598f282cc015f7020446602bd656d80c92fe5c1c2d5b9a","schema_version":"1.0","event_id":"sha256:4507615a99b8f4f9bf598f282cc015f7020446602bd656d80c92fe5c1c2d5b9a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:4EWPGX7DL4JJTG2WYAMTGUDEM2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Sound and Complete Algorithm for Learning Causal Models from Relational Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"David Arbour, David Jensen, Katerina Marazopoulou, Marc Maier","submitted_at":"2013-09-26T12:43:12Z","abstract_excerpt":"The PC algorithm learns maximally oriented causal Bayesian networks. However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks. Recent developments in the theory and representation of relational models support lifted reasoning about conditional independence. This enables a powerful constraint for orienting bivariate dependencies and forms the basis of a new algorithm for learning structure. We present the relational causal discovery (RCD) algorithm that learns causal relational models. We prove that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.6843","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:12:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rcdz1bqybB3q31pg+ZCRQ/KW0cEoEK3tsFvXrnLElKDq0ab6uZH87sJH0njXxl+g8B9LWvPnm7vTClZDj+kTDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T11:20:41.061845Z"},"content_sha256":"e7232aa1b45ca024dd7d6ac4eaaf18827fe906afe04eaa1390ab5deebbf206e1","schema_version":"1.0","event_id":"sha256:e7232aa1b45ca024dd7d6ac4eaaf18827fe906afe04eaa1390ab5deebbf206e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4EWPGX7DL4JJTG2WYAMTGUDEM2/bundle.json","state_url":"https://pith.science/pith/4EWPGX7DL4JJTG2WYAMTGUDEM2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4EWPGX7DL4JJTG2WYAMTGUDEM2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T11:20:41Z","links":{"resolver":"https://pith.science/pith/4EWPGX7DL4JJTG2WYAMTGUDEM2","bundle":"https://pith.science/pith/4EWPGX7DL4JJTG2WYAMTGUDEM2/bundle.json","state":"https://pith.science/pith/4EWPGX7DL4JJTG2WYAMTGUDEM2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4EWPGX7DL4JJTG2WYAMTGUDEM2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:4EWPGX7DL4JJTG2WYAMTGUDEM2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"65c4959dbdb9e4a97256ad1b0012d79a70c58b828538f451d6c1d0b5c8662266","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-09-26T12:43:12Z","title_canon_sha256":"918ff613c9ebb998b3ff27e95dfae7fbe3674d0c37cce4d563a76e0fcb012f7b"},"schema_version":"1.0","source":{"id":"1309.6843","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1309.6843","created_at":"2026-05-18T03:12:10Z"},{"alias_kind":"arxiv_version","alias_value":"1309.6843v1","created_at":"2026-05-18T03:12:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.6843","created_at":"2026-05-18T03:12:10Z"},{"alias_kind":"pith_short_12","alias_value":"4EWPGX7DL4JJ","created_at":"2026-05-18T12:27:32Z"},{"alias_kind":"pith_short_16","alias_value":"4EWPGX7DL4JJTG2W","created_at":"2026-05-18T12:27:32Z"},{"alias_kind":"pith_short_8","alias_value":"4EWPGX7D","created_at":"2026-05-18T12:27:32Z"}],"graph_snapshots":[{"event_id":"sha256:e7232aa1b45ca024dd7d6ac4eaaf18827fe906afe04eaa1390ab5deebbf206e1","target":"graph","created_at":"2026-05-18T03:12:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The PC algorithm learns maximally oriented causal Bayesian networks. However, there is no equivalent complete algorithm for learning the structure of relational models, a more expressive generalization of Bayesian networks. Recent developments in the theory and representation of relational models support lifted reasoning about conditional independence. This enables a powerful constraint for orienting bivariate dependencies and forms the basis of a new algorithm for learning structure. We present the relational causal discovery (RCD) algorithm that learns causal relational models. We prove that","authors_text":"David Arbour, David Jensen, Katerina Marazopoulou, Marc Maier","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-09-26T12:43:12Z","title":"A Sound and Complete Algorithm for Learning Causal Models from Relational Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.6843","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4507615a99b8f4f9bf598f282cc015f7020446602bd656d80c92fe5c1c2d5b9a","target":"record","created_at":"2026-05-18T03:12:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"65c4959dbdb9e4a97256ad1b0012d79a70c58b828538f451d6c1d0b5c8662266","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2013-09-26T12:43:12Z","title_canon_sha256":"918ff613c9ebb998b3ff27e95dfae7fbe3674d0c37cce4d563a76e0fcb012f7b"},"schema_version":"1.0","source":{"id":"1309.6843","kind":"arxiv","version":1}},"canonical_sha256":"e12cf35fe35f12999b56c019335064669ea18248ca618429ca0dd71f72ccef4d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e12cf35fe35f12999b56c019335064669ea18248ca618429ca0dd71f72ccef4d","first_computed_at":"2026-05-18T03:12:10.146860Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:12:10.146860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Y0RsdbSXcSSHmEOidR/NPmGH4S/gt9VqQR2Y2gK2pfS/kaWd4824J61HWM8aKgwbdIlUq3f7g0b8VZqqKNNzBw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:12:10.147534Z","signed_message":"canonical_sha256_bytes"},"source_id":"1309.6843","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4507615a99b8f4f9bf598f282cc015f7020446602bd656d80c92fe5c1c2d5b9a","sha256:e7232aa1b45ca024dd7d6ac4eaaf18827fe906afe04eaa1390ab5deebbf206e1"],"state_sha256":"98b380afb5786708726411db89f7beb0f5a21a7abaf61292625a344574531bd9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wwdLWC/6hoFJBESTKnqENpxlDT4kgzjqU2T2SfQXrgRzhDvxxaN9CARfZ+jNBczncvPeBQAjTK4LCWujWXM4Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T11:20:41.063752Z","bundle_sha256":"a47923a82bc528ca61d8a2a3fbffce904cc336c18851963ee032223b4d4924f0"}}