{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TQQADB3UPLMTNWIQ2ZV2MBGSNH","short_pith_number":"pith:TQQADB3U","schema_version":"1.0","canonical_sha256":"9c200187747ad936d910d66ba604d269d3459f1b34fcaaf9f2a5f87a1474cdd2","source":{"kind":"arxiv","id":"2211.14897","version":4},"attestation_state":"computed","paper":{"title":"Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Armeen Taeb, Christina Heinze-Deml, Juan L. Gamella, Peter B\\\"uhlmann","submitted_at":"2022-11-27T17:37:21Z","abstract_excerpt":"We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships in the system. Different models may entail the same distributions, offering competing causal explanations for the given observations. We fully characterize this equivalence class and offer identifiability results, which we use to derive a "},"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":"2211.14897","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-11-27T17:37:21Z","cross_cats_sorted":["stat.CO","stat.ML"],"title_canon_sha256":"635009a052313cd92b91028f0f3f8919cfa6f12f462d5e77ad21715ebe8927e5","abstract_canon_sha256":"d0e0e83fdfad2b98c19ec71630112a4cf2778ade9d97d634ba47c96f12d854fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:29:11.752407Z","signature_b64":"7KEDK8LB1SzzsGAnxmIRgJFu99M07T0Ki/qbwLtczaTD4i2S4f1N/53Sp6IYP5x1jJZjrQJWKCXhSq7tvNmpAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c200187747ad936d910d66ba604d269d3459f1b34fcaaf9f2a5f87a1474cdd2","last_reissued_at":"2026-07-05T10:29:11.751756Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:29:11.751756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Armeen Taeb, Christina Heinze-Deml, Juan L. Gamella, Peter B\\\"uhlmann","submitted_at":"2022-11-27T17:37:21Z","abstract_excerpt":"We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships in the system. Different models may entail the same distributions, offering competing causal explanations for the given observations. We fully characterize this equivalence class and offer identifiability results, which we use to derive a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.14897","kind":"arxiv","version":4},"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/2211.14897/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.14897","created_at":"2026-07-05T10:29:11.751835+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.14897v4","created_at":"2026-07-05T10:29:11.751835+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.14897","created_at":"2026-07-05T10:29:11.751835+00:00"},{"alias_kind":"pith_short_12","alias_value":"TQQADB3UPLMT","created_at":"2026-07-05T10:29:11.751835+00:00"},{"alias_kind":"pith_short_16","alias_value":"TQQADB3UPLMTNWIQ","created_at":"2026-07-05T10:29:11.751835+00:00"},{"alias_kind":"pith_short_8","alias_value":"TQQADB3U","created_at":"2026-07-05T10:29:11.751835+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.14003","citing_title":"Generative Intervention Models for Causal Perturbation Modeling","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH","json":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH.json","graph_json":"https://pith.science/api/pith-number/TQQADB3UPLMTNWIQ2ZV2MBGSNH/graph.json","events_json":"https://pith.science/api/pith-number/TQQADB3UPLMTNWIQ2ZV2MBGSNH/events.json","paper":"https://pith.science/paper/TQQADB3U"},"agent_actions":{"view_html":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH","download_json":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH.json","view_paper":"https://pith.science/paper/TQQADB3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.14897&json=true","fetch_graph":"https://pith.science/api/pith-number/TQQADB3UPLMTNWIQ2ZV2MBGSNH/graph.json","fetch_events":"https://pith.science/api/pith-number/TQQADB3UPLMTNWIQ2ZV2MBGSNH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH/action/storage_attestation","attest_author":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH/action/author_attestation","sign_citation":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH/action/citation_signature","submit_replication":"https://pith.science/pith/TQQADB3UPLMTNWIQ2ZV2MBGSNH/action/replication_record"}},"created_at":"2026-07-05T10:29:11.751835+00:00","updated_at":"2026-07-05T10:29:11.751835+00:00"}