{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NZ7ZXIULNIEWXGAM62MUNRJQII","short_pith_number":"pith:NZ7ZXIUL","canonical_record":{"source":{"id":"2506.03068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-03T16:46:13Z","cross_cats_sorted":["cs.CY","cs.LG"],"title_canon_sha256":"a3a2ef5e77be594939396dc4068a5a551c1a860f9f8fc507f1a2cd9a090564e5","abstract_canon_sha256":"adb7cb8e2b5b391375a1c3b3604b1bacb26283398481bc4b5ef064bc91f9569c"},"schema_version":"1.0"},"canonical_sha256":"6e7f9ba28b6a096b980cf69946c530423b789264fb429e331bc2119c897f3aef","source":{"kind":"arxiv","id":"2506.03068","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.03068","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"arxiv_version","alias_value":"2506.03068v1","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03068","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"pith_short_12","alias_value":"NZ7ZXIULNIEW","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"pith_short_16","alias_value":"NZ7ZXIULNIEWXGAM","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"pith_short_8","alias_value":"NZ7ZXIUL","created_at":"2026-07-05T11:15:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NZ7ZXIULNIEWXGAM62MUNRJQII","target":"record","payload":{"canonical_record":{"source":{"id":"2506.03068","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-03T16:46:13Z","cross_cats_sorted":["cs.CY","cs.LG"],"title_canon_sha256":"a3a2ef5e77be594939396dc4068a5a551c1a860f9f8fc507f1a2cd9a090564e5","abstract_canon_sha256":"adb7cb8e2b5b391375a1c3b3604b1bacb26283398481bc4b5ef064bc91f9569c"},"schema_version":"1.0"},"canonical_sha256":"6e7f9ba28b6a096b980cf69946c530423b789264fb429e331bc2119c897f3aef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:14.325719Z","signature_b64":"mpsv9t6/iGMaMqnLhgUZodzWS+hTdY5UZfPSobl2Ma9u2t4u2hOST4oqu7FHNC4L7h8zonHfIVMClkjIMCgBAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e7f9ba28b6a096b980cf69946c530423b789264fb429e331bc2119c897f3aef","last_reissued_at":"2026-07-05T11:15:14.325246Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:14.325246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.03068","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":"2026-07-05T11:15:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n0psSnPouiPV71q/m834hQka7+NTr0/M+xDfYttwTWtd+ItqhbCVJVClpTZK+TN2qITC14/SbVOrXbhmFLGyCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:54:28.467444Z"},"content_sha256":"0a81834779e5b092be0bd9aba6a88e43fa3b470feda32408a0aabd9cc74a8314","schema_version":"1.0","event_id":"sha256:0a81834779e5b092be0bd9aba6a88e43fa3b470feda32408a0aabd9cc74a8314"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NZ7ZXIULNIEWXGAM62MUNRJQII","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"stat.ML","authors_text":"Liang Hong, Manar D. Samad, Norou Diawara, Shourav B. Rabbani, Yina Hou","submitted_at":"2025-06-03T16:46:13Z","abstract_excerpt":"The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML). However, the predictive importance of these variables may not represent their causal relationships with diseases. This paper uses clinical variables from a heart failure (HF) patient cohort to investigate the causal explainability of important variables obtained in statistical and ML contexts. Due to inherent regression modeling, popular causal discovery methods strictly assume that the cause and effect variables are numerical and continuous. This paper prop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03068","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/2506.03068/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:15:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fcw+Kf8gEwrVF8wh39HOW9OrXiqwSrKrLpsz9K+5Bu/3xZHSydM4lBKJjM+047RIU+TIb1hZeG07w5T4wIzaAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T05:54:28.467959Z"},"content_sha256":"20ed9fb28ae8485c30fbada7f37c8fe7410bae7877914f08ba00caa53ec374aa","schema_version":"1.0","event_id":"sha256:20ed9fb28ae8485c30fbada7f37c8fe7410bae7877914f08ba00caa53ec374aa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NZ7ZXIULNIEWXGAM62MUNRJQII/bundle.json","state_url":"https://pith.science/pith/NZ7ZXIULNIEWXGAM62MUNRJQII/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NZ7ZXIULNIEWXGAM62MUNRJQII/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":"2026-08-08T05:54:28Z","links":{"resolver":"https://pith.science/pith/NZ7ZXIULNIEWXGAM62MUNRJQII","bundle":"https://pith.science/pith/NZ7ZXIULNIEWXGAM62MUNRJQII/bundle.json","state":"https://pith.science/pith/NZ7ZXIULNIEWXGAM62MUNRJQII/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NZ7ZXIULNIEWXGAM62MUNRJQII/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NZ7ZXIULNIEWXGAM62MUNRJQII","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":"adb7cb8e2b5b391375a1c3b3604b1bacb26283398481bc4b5ef064bc91f9569c","cross_cats_sorted":["cs.CY","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-03T16:46:13Z","title_canon_sha256":"a3a2ef5e77be594939396dc4068a5a551c1a860f9f8fc507f1a2cd9a090564e5"},"schema_version":"1.0","source":{"id":"2506.03068","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.03068","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"arxiv_version","alias_value":"2506.03068v1","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03068","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"pith_short_12","alias_value":"NZ7ZXIULNIEW","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"pith_short_16","alias_value":"NZ7ZXIULNIEWXGAM","created_at":"2026-07-05T11:15:14Z"},{"alias_kind":"pith_short_8","alias_value":"NZ7ZXIUL","created_at":"2026-07-05T11:15:14Z"}],"graph_snapshots":[{"event_id":"sha256:20ed9fb28ae8485c30fbada7f37c8fe7410bae7877914f08ba00caa53ec374aa","target":"graph","created_at":"2026-07-05T11:15:14Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.03068/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML). However, the predictive importance of these variables may not represent their causal relationships with diseases. This paper uses clinical variables from a heart failure (HF) patient cohort to investigate the causal explainability of important variables obtained in statistical and ML contexts. Due to inherent regression modeling, popular causal discovery methods strictly assume that the cause and effect variables are numerical and continuous. This paper prop","authors_text":"Liang Hong, Manar D. Samad, Norou Diawara, Shourav B. Rabbani, Yina Hou","cross_cats":["cs.CY","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-03T16:46:13Z","title":"Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03068","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":"sha256:0a81834779e5b092be0bd9aba6a88e43fa3b470feda32408a0aabd9cc74a8314","target":"record","created_at":"2026-07-05T11:15:14Z","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":"adb7cb8e2b5b391375a1c3b3604b1bacb26283398481bc4b5ef064bc91f9569c","cross_cats_sorted":["cs.CY","cs.LG"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-03T16:46:13Z","title_canon_sha256":"a3a2ef5e77be594939396dc4068a5a551c1a860f9f8fc507f1a2cd9a090564e5"},"schema_version":"1.0","source":{"id":"2506.03068","kind":"arxiv","version":1}},"canonical_sha256":"6e7f9ba28b6a096b980cf69946c530423b789264fb429e331bc2119c897f3aef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e7f9ba28b6a096b980cf69946c530423b789264fb429e331bc2119c897f3aef","first_computed_at":"2026-07-05T11:15:14.325246Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:15:14.325246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mpsv9t6/iGMaMqnLhgUZodzWS+hTdY5UZfPSobl2Ma9u2t4u2hOST4oqu7FHNC4L7h8zonHfIVMClkjIMCgBAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:15:14.325719Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.03068","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a81834779e5b092be0bd9aba6a88e43fa3b470feda32408a0aabd9cc74a8314","sha256:20ed9fb28ae8485c30fbada7f37c8fe7410bae7877914f08ba00caa53ec374aa"],"state_sha256":"54494f0d86b3bacab313d1df7c85a0a745ff57efee0fb1b267e6405a0ac0693b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"epUKhM/iChyoSVT7ZM5C7jzMCYk6DVqZh28ykT3m1q41Y1/zUrGQZyuKQa11vVyWhvesbUmZ3F/xdlV4G4M+BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T05:54:28.473173Z","bundle_sha256":"8183abca7fdda0e8614ba0a99ae83643deb325256c9616f42da3f1de9a523d5c"}}