{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IKFSSFOZ7FCRO46H4MCU6AUMVR","short_pith_number":"pith:IKFSSFOZ","schema_version":"1.0","canonical_sha256":"428b2915d9f9451773c7e3054f028cac5d6acd4eb218aaf7567d418319a7ec8b","source":{"kind":"arxiv","id":"2311.04585","version":1},"attestation_state":"computed","paper":{"title":"Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Daniela Schkoda, Mathias Drton","submitted_at":"2023-11-08T10:27:50Z","abstract_excerpt":"The field of causal discovery develops model selection methods to infer cause-effect relations among a set of random variables. For this purpose, different modelling assumptions have been proposed to render cause-effect relations identifiable. One prominent assumption is that the joint distribution of the observed variables follows a linear non-Gaussian structural equation model. In this paper, we develop novel goodness-of-fit tests that assess the validity of this assumption in the basic setting without latent confounders as well as in extension to linear models that incorporate latent confou"},"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":"2311.04585","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-11-08T10:27:50Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"be18669b7c7ccdc263d4592099f7da4952afc8e7db72b6ec62a1a88839d69de7","abstract_canon_sha256":"cdca7fcfca6489b559e34ca0fe20b1116a22cf1945ce6fe1b6d230f8bc981d9d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:10:38.612242Z","signature_b64":"XbOSFt+ui2PDo03Svfo3J3R6ZfTIXbi93EQHUcSvH0s23IoKj7BuaIplQOYAiQPsZ4ctataQrs4F6j15ftgsBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"428b2915d9f9451773c7e3054f028cac5d6acd4eb218aaf7567d418319a7ec8b","last_reissued_at":"2026-07-05T07:10:38.611763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:10:38.611763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Goodness-of-Fit Tests for Linear Non-Gaussian Structural Equation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Daniela Schkoda, Mathias Drton","submitted_at":"2023-11-08T10:27:50Z","abstract_excerpt":"The field of causal discovery develops model selection methods to infer cause-effect relations among a set of random variables. For this purpose, different modelling assumptions have been proposed to render cause-effect relations identifiable. One prominent assumption is that the joint distribution of the observed variables follows a linear non-Gaussian structural equation model. In this paper, we develop novel goodness-of-fit tests that assess the validity of this assumption in the basic setting without latent confounders as well as in extension to linear models that incorporate latent confou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.04585","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/2311.04585/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":"2311.04585","created_at":"2026-07-05T07:10:38.611821+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.04585v1","created_at":"2026-07-05T07:10:38.611821+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.04585","created_at":"2026-07-05T07:10:38.611821+00:00"},{"alias_kind":"pith_short_12","alias_value":"IKFSSFOZ7FCR","created_at":"2026-07-05T07:10:38.611821+00:00"},{"alias_kind":"pith_short_16","alias_value":"IKFSSFOZ7FCRO46H","created_at":"2026-07-05T07:10:38.611821+00:00"},{"alias_kind":"pith_short_8","alias_value":"IKFSSFOZ","created_at":"2026-07-05T07:10:38.611821+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR","json":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR.json","graph_json":"https://pith.science/api/pith-number/IKFSSFOZ7FCRO46H4MCU6AUMVR/graph.json","events_json":"https://pith.science/api/pith-number/IKFSSFOZ7FCRO46H4MCU6AUMVR/events.json","paper":"https://pith.science/paper/IKFSSFOZ"},"agent_actions":{"view_html":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR","download_json":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR.json","view_paper":"https://pith.science/paper/IKFSSFOZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.04585&json=true","fetch_graph":"https://pith.science/api/pith-number/IKFSSFOZ7FCRO46H4MCU6AUMVR/graph.json","fetch_events":"https://pith.science/api/pith-number/IKFSSFOZ7FCRO46H4MCU6AUMVR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR/action/storage_attestation","attest_author":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR/action/author_attestation","sign_citation":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR/action/citation_signature","submit_replication":"https://pith.science/pith/IKFSSFOZ7FCRO46H4MCU6AUMVR/action/replication_record"}},"created_at":"2026-07-05T07:10:38.611821+00:00","updated_at":"2026-07-05T07:10:38.611821+00:00"}