{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:WAMG6MPEUNFDKG75VISLPXP5SS","short_pith_number":"pith:WAMG6MPE","schema_version":"1.0","canonical_sha256":"b0186f31e4a34a351bfdaa24b7ddfd94809b4d3545c6800e0c9bb5c66c9665ff","source":{"kind":"arxiv","id":"1608.08028","version":2},"attestation_state":"computed","paper":{"title":"From Deterministic ODEs to Dynamic Structural Causal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bernhard Schoelkopf, Joris M. Mooij, Paul K. Rubenstein, Stephan Bongers","submitted_at":"2016-08-29T12:43:42Z","abstract_excerpt":"Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood. In this paper we provide a novel perspective on the relationship between Ordinary Differential Equations and Structural Causal Models. We show how, under certain conditions, the asymptotic behaviour of an Ordinary Differential Equation under non-constant interventions can be modelled using Dynamic Structural Causal Models. In contrast to earlier work, we study not only the effect of interventions on equilibrium states; rather, we model asymptotic behaviour that is dy"},"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":"1608.08028","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-08-29T12:43:42Z","cross_cats_sorted":[],"title_canon_sha256":"6173b948a11afb96c8d1ae2dbedd608cf7e071748b8cfd33854e7a84308edf7f","abstract_canon_sha256":"a30a85c6581a200f47d6145a01156742f47b164c73637a9e00653788ba78d3b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:52:20.882992Z","signature_b64":"A37+k6ARSJMFy0SpUy4Spk1ymO4to2csAoI+QjXxAhXDg/QUDPcirLBzbTc0C06DRnrPhV31xN+JvprAB98+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0186f31e4a34a351bfdaa24b7ddfd94809b4d3545c6800e0c9bb5c66c9665ff","last_reissued_at":"2026-07-05T04:52:20.882492Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:52:20.882492Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Deterministic ODEs to Dynamic Structural Causal Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bernhard Schoelkopf, Joris M. Mooij, Paul K. Rubenstein, Stephan Bongers","submitted_at":"2016-08-29T12:43:42Z","abstract_excerpt":"Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood. In this paper we provide a novel perspective on the relationship between Ordinary Differential Equations and Structural Causal Models. We show how, under certain conditions, the asymptotic behaviour of an Ordinary Differential Equation under non-constant interventions can be modelled using Dynamic Structural Causal Models. In contrast to earlier work, we study not only the effect of interventions on equilibrium states; rather, we model asymptotic behaviour that is dy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.08028","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/1608.08028/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":"1608.08028","created_at":"2026-07-05T04:52:20.882552+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.08028v2","created_at":"2026-07-05T04:52:20.882552+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.08028","created_at":"2026-07-05T04:52:20.882552+00:00"},{"alias_kind":"pith_short_12","alias_value":"WAMG6MPEUNFD","created_at":"2026-07-05T04:52:20.882552+00:00"},{"alias_kind":"pith_short_16","alias_value":"WAMG6MPEUNFDKG75","created_at":"2026-07-05T04:52:20.882552+00:00"},{"alias_kind":"pith_short_8","alias_value":"WAMG6MPE","created_at":"2026-07-05T04:52:20.882552+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07319","citing_title":"Generative Modeling with Flux Matching","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00398","citing_title":"M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data","ref_index":114,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS","json":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS.json","graph_json":"https://pith.science/api/pith-number/WAMG6MPEUNFDKG75VISLPXP5SS/graph.json","events_json":"https://pith.science/api/pith-number/WAMG6MPEUNFDKG75VISLPXP5SS/events.json","paper":"https://pith.science/paper/WAMG6MPE"},"agent_actions":{"view_html":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS","download_json":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS.json","view_paper":"https://pith.science/paper/WAMG6MPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.08028&json=true","fetch_graph":"https://pith.science/api/pith-number/WAMG6MPEUNFDKG75VISLPXP5SS/graph.json","fetch_events":"https://pith.science/api/pith-number/WAMG6MPEUNFDKG75VISLPXP5SS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS/action/storage_attestation","attest_author":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS/action/author_attestation","sign_citation":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS/action/citation_signature","submit_replication":"https://pith.science/pith/WAMG6MPEUNFDKG75VISLPXP5SS/action/replication_record"}},"created_at":"2026-07-05T04:52:20.882552+00:00","updated_at":"2026-07-05T04:52:20.882552+00:00"}