{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:266REUGYMQXH7MWTU355G4UJKH","short_pith_number":"pith:266REUGY","schema_version":"1.0","canonical_sha256":"d7bd1250d8642e7fb2d3a6fbd3728951e46663d6d32a179ce1f577b3a36227c6","source":{"kind":"arxiv","id":"2504.14937","version":1},"attestation_state":"computed","paper":{"title":"Causal DAG Summarization (Full Version)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB","stat.ME"],"primary_cat":"cs.LG","authors_text":"Anna Zeng, Babak Salimi, Batya Kenig, Brit Youngmann, Markos Markakis, Michael Cafarella","submitted_at":"2025-04-21T08:01:32Z","abstract_excerpt":"Causal inference aids researchers in discovering cause-and-effect relationships, leading to scientific insights. Accurate causal estimation requires identifying confounding variables to avoid false discoveries. Pearl's causal model uses causal DAGs to identify confounding variables, but incorrect DAGs can lead to unreliable causal conclusions. However, for high dimensional data, the causal DAGs are often complex beyond human verifiability. Graph summarization is a logical next step, but current methods for general-purpose graph summarization are inadequate for causal DAG summarization. This pa"},"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":"2504.14937","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-21T08:01:32Z","cross_cats_sorted":["cs.DB","stat.ME"],"title_canon_sha256":"375c8fe40adeee96ebb76156836bee30ef1b2d3371cce270ee6f018f9728c096","abstract_canon_sha256":"df6799e4c8f9d3061cd01059a3e3a3ded07be7b35adfdd0fb86e411c99fa65c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:53.657341Z","signature_b64":"snxVphfVebbQSigllrGaUOeHMIIcWvem6fMzLFG6+G2caNNux+t5A2KM5+S5XMaZjiN4Fhuhlp17IZHSVFJgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7bd1250d8642e7fb2d3a6fbd3728951e46663d6d32a179ce1f577b3a36227c6","last_reissued_at":"2026-07-05T10:51:53.655727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:53.655727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causal DAG Summarization (Full Version)","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.DB","stat.ME"],"primary_cat":"cs.LG","authors_text":"Anna Zeng, Babak Salimi, Batya Kenig, Brit Youngmann, Markos Markakis, Michael Cafarella","submitted_at":"2025-04-21T08:01:32Z","abstract_excerpt":"Causal inference aids researchers in discovering cause-and-effect relationships, leading to scientific insights. Accurate causal estimation requires identifying confounding variables to avoid false discoveries. Pearl's causal model uses causal DAGs to identify confounding variables, but incorrect DAGs can lead to unreliable causal conclusions. However, for high dimensional data, the causal DAGs are often complex beyond human verifiability. Graph summarization is a logical next step, but current methods for general-purpose graph summarization are inadequate for causal DAG summarization. This pa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14937","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/2504.14937/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":"2504.14937","created_at":"2026-07-05T10:51:53.655815+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.14937v1","created_at":"2026-07-05T10:51:53.655815+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14937","created_at":"2026-07-05T10:51:53.655815+00:00"},{"alias_kind":"pith_short_12","alias_value":"266REUGYMQXH","created_at":"2026-07-05T10:51:53.655815+00:00"},{"alias_kind":"pith_short_16","alias_value":"266REUGYMQXH7MWT","created_at":"2026-07-05T10:51:53.655815+00:00"},{"alias_kind":"pith_short_8","alias_value":"266REUGY","created_at":"2026-07-05T10:51:53.655815+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.07501","citing_title":"Graph-of-Causal Evolution: Challenging Chain-of-Model for Reasoning","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH","json":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH.json","graph_json":"https://pith.science/api/pith-number/266REUGYMQXH7MWTU355G4UJKH/graph.json","events_json":"https://pith.science/api/pith-number/266REUGYMQXH7MWTU355G4UJKH/events.json","paper":"https://pith.science/paper/266REUGY"},"agent_actions":{"view_html":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH","download_json":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH.json","view_paper":"https://pith.science/paper/266REUGY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.14937&json=true","fetch_graph":"https://pith.science/api/pith-number/266REUGYMQXH7MWTU355G4UJKH/graph.json","fetch_events":"https://pith.science/api/pith-number/266REUGYMQXH7MWTU355G4UJKH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH/action/storage_attestation","attest_author":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH/action/author_attestation","sign_citation":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH/action/citation_signature","submit_replication":"https://pith.science/pith/266REUGYMQXH7MWTU355G4UJKH/action/replication_record"}},"created_at":"2026-07-05T10:51:53.655815+00:00","updated_at":"2026-07-05T10:51:53.655815+00:00"}