{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:2HOW4GDPDHYGCRJILGMHQNGDTO","short_pith_number":"pith:2HOW4GDP","canonical_record":{"source":{"id":"2411.06990","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-11T13:48:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"56d3da0351c54baae73a01836f86e8ac53e50b739d1e199640ec7f666330e1f8","abstract_canon_sha256":"c0cad20cdb55603e0fd2d2474e36d09266a5caab00629c20766ebbfd309a8e0a"},"schema_version":"1.0"},"canonical_sha256":"d1dd6e186f19f061452859987834c39ba94c1b19e0cacc42670be731bff686e8","source":{"kind":"arxiv","id":"2411.06990","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.06990","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"arxiv_version","alias_value":"2411.06990v2","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.06990","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"pith_short_12","alias_value":"2HOW4GDPDHYG","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"pith_short_16","alias_value":"2HOW4GDPDHYGCRJI","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"pith_short_8","alias_value":"2HOW4GDP","created_at":"2026-07-05T10:54:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:2HOW4GDPDHYGCRJILGMHQNGDTO","target":"record","payload":{"canonical_record":{"source":{"id":"2411.06990","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-11T13:48:13Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"56d3da0351c54baae73a01836f86e8ac53e50b739d1e199640ec7f666330e1f8","abstract_canon_sha256":"c0cad20cdb55603e0fd2d2474e36d09266a5caab00629c20766ebbfd309a8e0a"},"schema_version":"1.0"},"canonical_sha256":"d1dd6e186f19f061452859987834c39ba94c1b19e0cacc42670be731bff686e8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:13.896840Z","signature_b64":"QqN8Te93oEPnj1Wc3Dtt5cv2LgpYNC3ie6v/E9looRYVNJ/r4TCMQTNVwlUU498GuS/+5VE3vCgSi5WKlf0vCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1dd6e186f19f061452859987834c39ba94c1b19e0cacc42670be731bff686e8","last_reissued_at":"2026-07-05T10:54:13.896254Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:13.896254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.06990","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:54:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1QqgXv44t4Mjmr5xIBP1KNBmpv0yElnRpvozq66iHmlmawV/BLN/8xGYAv1H86iw6ZGXi751VGBrZ+MD0lmmCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:09:01.235619Z"},"content_sha256":"6fb6e3e1c0d50f6e9a66887feb78d48e6dfa16f12fbe5b65a8986128c9b90be6","schema_version":"1.0","event_id":"sha256:6fb6e3e1c0d50f6e9a66887feb78d48e6dfa16f12fbe5b65a8986128c9b90be6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:2HOW4GDPDHYGCRJILGMHQNGDTO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Hiroshi Yokoyama, Kaneharu Nishino, Ryusei Shingaki, Shohei Shimizu, Thong Pham","submitted_at":"2024-11-11T13:48:13Z","abstract_excerpt":"Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain \"black boxes\", making prediction error diagnosis challenging, especially with outliers. This lack of transparency hinders trust and reliability in industrial applications. Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. Various root-cause analysis methods have been developed using Shapley values, yet they typically require predefined causal graphs, limiting their applicability for p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.06990","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.06990/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":"2026-07-05T10:54:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B5pZgEOxiFrFB6KyfeCVkSE+XknFKzQi/fgf4KMjSA/3G2drHwG3c5ktljDFF11NSY278GHlUwh9RicwErgfCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T01:09:01.236541Z"},"content_sha256":"ceaf1441c54ee4c6b31dc9d7f6ee23f6dcb5f30c1bcc9c895bd2d82d779fd3b1","schema_version":"1.0","event_id":"sha256:ceaf1441c54ee4c6b31dc9d7f6ee23f6dcb5f30c1bcc9c895bd2d82d779fd3b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2HOW4GDPDHYGCRJILGMHQNGDTO/bundle.json","state_url":"https://pith.science/pith/2HOW4GDPDHYGCRJILGMHQNGDTO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2HOW4GDPDHYGCRJILGMHQNGDTO/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":"2026-08-09T01:09:01Z","links":{"resolver":"https://pith.science/pith/2HOW4GDPDHYGCRJILGMHQNGDTO","bundle":"https://pith.science/pith/2HOW4GDPDHYGCRJILGMHQNGDTO/bundle.json","state":"https://pith.science/pith/2HOW4GDPDHYGCRJILGMHQNGDTO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2HOW4GDPDHYGCRJILGMHQNGDTO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2HOW4GDPDHYGCRJILGMHQNGDTO","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":"c0cad20cdb55603e0fd2d2474e36d09266a5caab00629c20766ebbfd309a8e0a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-11T13:48:13Z","title_canon_sha256":"56d3da0351c54baae73a01836f86e8ac53e50b739d1e199640ec7f666330e1f8"},"schema_version":"1.0","source":{"id":"2411.06990","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.06990","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"arxiv_version","alias_value":"2411.06990v2","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.06990","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"pith_short_12","alias_value":"2HOW4GDPDHYG","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"pith_short_16","alias_value":"2HOW4GDPDHYGCRJI","created_at":"2026-07-05T10:54:13Z"},{"alias_kind":"pith_short_8","alias_value":"2HOW4GDP","created_at":"2026-07-05T10:54:13Z"}],"graph_snapshots":[{"event_id":"sha256:ceaf1441c54ee4c6b31dc9d7f6ee23f6dcb5f30c1bcc9c895bd2d82d779fd3b1","target":"graph","created_at":"2026-07-05T10:54:13Z","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/2411.06990/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain \"black boxes\", making prediction error diagnosis challenging, especially with outliers. This lack of transparency hinders trust and reliability in industrial applications. Heuristic attribution methods, while helpful, often fail to capture true causal relationships, leading to inaccurate error attributions. Various root-cause analysis methods have been developed using Shapley values, yet they typically require predefined causal graphs, limiting their applicability for p","authors_text":"Hiroshi Yokoyama, Kaneharu Nishino, Ryusei Shingaki, Shohei Shimizu, Thong Pham","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-11T13:48:13Z","title":"Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.06990","kind":"arxiv","version":2},"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":"sha256:6fb6e3e1c0d50f6e9a66887feb78d48e6dfa16f12fbe5b65a8986128c9b90be6","target":"record","created_at":"2026-07-05T10:54:13Z","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":"c0cad20cdb55603e0fd2d2474e36d09266a5caab00629c20766ebbfd309a8e0a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-11-11T13:48:13Z","title_canon_sha256":"56d3da0351c54baae73a01836f86e8ac53e50b739d1e199640ec7f666330e1f8"},"schema_version":"1.0","source":{"id":"2411.06990","kind":"arxiv","version":2}},"canonical_sha256":"d1dd6e186f19f061452859987834c39ba94c1b19e0cacc42670be731bff686e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d1dd6e186f19f061452859987834c39ba94c1b19e0cacc42670be731bff686e8","first_computed_at":"2026-07-05T10:54:13.896254Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:54:13.896254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QqN8Te93oEPnj1Wc3Dtt5cv2LgpYNC3ie6v/E9looRYVNJ/r4TCMQTNVwlUU498GuS/+5VE3vCgSi5WKlf0vCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:54:13.896840Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.06990","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6fb6e3e1c0d50f6e9a66887feb78d48e6dfa16f12fbe5b65a8986128c9b90be6","sha256:ceaf1441c54ee4c6b31dc9d7f6ee23f6dcb5f30c1bcc9c895bd2d82d779fd3b1"],"state_sha256":"5b321e209ee382edaac6def5785f7851102320d7e71c1a55544098e404cc48d0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QVUiCfXQteXdlg4vXrxJskMidmgPSRXHPyTkjHajLdRCimTPgfUTjRrQ+H0B99+a+HlJfVsxnSVEHidFW7S2DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T01:09:01.242373Z","bundle_sha256":"36b6aff54f6672e172b6cc58518cc7e82a5912b708fe248f7a8cb34d7e2be212"}}