{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:HARM4FCGWNYMF52HXD6ZPIK3EE","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":"b65c25646b0c93e735ad176adf249791d7a2780defcae1b249c5d00eaa611967","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-10-25T13:59:42Z","title_canon_sha256":"662e6a8165d7590ed5aa6d84aaa97e20b166562755783a0332980e30775ce67d"},"schema_version":"1.0","source":{"id":"2510.22298","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.22298","created_at":"2026-07-07T00:15:50Z"},{"alias_kind":"arxiv_version","alias_value":"2510.22298v2","created_at":"2026-07-07T00:15:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.22298","created_at":"2026-07-07T00:15:50Z"},{"alias_kind":"pith_short_12","alias_value":"HARM4FCGWNYM","created_at":"2026-07-07T00:15:50Z"},{"alias_kind":"pith_short_16","alias_value":"HARM4FCGWNYMF52H","created_at":"2026-07-07T00:15:50Z"},{"alias_kind":"pith_short_8","alias_value":"HARM4FCG","created_at":"2026-07-07T00:15:50Z"}],"graph_snapshots":[{"event_id":"sha256:96798176a7518593b901465b4230840b976f91554f65bed15d4309dc7cea69f6","target":"graph","created_at":"2026-07-07T00:15:50Z","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/2510.22298/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Uncovering the causal mechanisms of complex real-world systems remains a significant challenge, as these systems often entail high data collection costs and involve unknown interventions. We introduce MetaCaDI, the first framework to cast the identification of unknown interventions as a meta-learning problem, explicitly leveraging a jointly learned causal graph. MetaCaDI is a Bayesian framework that learns a shared causal structure across multiple environments and is optimized to rapidly adapt to new, few-shot intervention target identification tasks. A key innovation is our model's analytical","authors_text":"Hans Jarett Ong, Tomoharu Iwata, Yoichi Chikahara","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-10-25T13:59:42Z","title":"MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.22298","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":"sha256:6fefea1abb42f42d7fe13b037e8e8973175c49870008b8a7939e90912444ada8","target":"record","created_at":"2026-07-07T00:15:50Z","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":"b65c25646b0c93e735ad176adf249791d7a2780defcae1b249c5d00eaa611967","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-10-25T13:59:42Z","title_canon_sha256":"662e6a8165d7590ed5aa6d84aaa97e20b166562755783a0332980e30775ce67d"},"schema_version":"1.0","source":{"id":"2510.22298","kind":"arxiv","version":2}},"canonical_sha256":"3822ce1446b370c2f747b8fd97a15b2132accc43c01edc9a9e9b649737533d84","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3822ce1446b370c2f747b8fd97a15b2132accc43c01edc9a9e9b649737533d84","first_computed_at":"2026-07-07T00:15:50.485265Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-07T00:15:50.485265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yu+0rAjeqZKlNZSXSZV/qX5NCjwNTNuue/ayqjshYfMhMoD+yOPrgdhC6aJ7S29qRZS+LhZeyPdeQZqLsgaCAA==","signature_status":"signed_v1","signed_at":"2026-07-07T00:15:50.486218Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.22298","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6fefea1abb42f42d7fe13b037e8e8973175c49870008b8a7939e90912444ada8","sha256:96798176a7518593b901465b4230840b976f91554f65bed15d4309dc7cea69f6"],"state_sha256":"6a8b84ae0e848e02d4e95619f4b64b2531f5f5d39d974b78bbf586ea78768e48"}