{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UDR2JC66FLEZAW6EF6J3PEDDGS","short_pith_number":"pith:UDR2JC66","schema_version":"1.0","canonical_sha256":"a0e3a48bde2ac9905bc42f93b7906334aaccc37add2ad3bc250fba92fdeffdbb","source":{"kind":"arxiv","id":"2101.07600","version":1},"attestation_state":"computed","paper":{"title":"Interpretable Models for Granger Causality Using Self-explaining Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Julia E. Vogt, Ri\\v{c}ards Marcinkevi\\v{c}s","submitted_at":"2021-01-19T12:59:00Z","abstract_excerpt":"Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks. This framework is more interpretable than other neural-network-based techniques for inferring Granger causality, since in addition to relational inference, it also allows detecting signs of Granger-"},"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":"2101.07600","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-01-19T12:59:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cdc541d225db5b27eda2661199c111cdbcfaf1df9d290c87dff626730e1c336a","abstract_canon_sha256":"cb2fb79f698aa3d59da604bd4937071fcd61a265edac1c09f5565cc027e07928"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:07:59.426546Z","signature_b64":"oGLvMso3QlZ2yxrntbE6B/NLMdjgs6Clg6B/FGZjAR21imw95DEXNhlia7kfGOS0sFmARVLPcev0L9jOS6sSAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0e3a48bde2ac9905bc42f93b7906334aaccc37add2ad3bc250fba92fdeffdbb","last_reissued_at":"2026-07-05T02:07:59.426118Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:07:59.426118Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpretable Models for Granger Causality Using Self-explaining Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Julia E. Vogt, Ri\\v{c}ards Marcinkevi\\v{c}s","submitted_at":"2021-01-19T12:59:00Z","abstract_excerpt":"Exploratory analysis of time series data can yield a better understanding of complex dynamical systems. Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains. In this paper, we propose a novel framework for inferring multivariate Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks. This framework is more interpretable than other neural-network-based techniques for inferring Granger causality, since in addition to relational inference, it also allows detecting signs of Granger-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.07600","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/2101.07600/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":"2101.07600","created_at":"2026-07-05T02:07:59.426178+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.07600v1","created_at":"2026-07-05T02:07:59.426178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.07600","created_at":"2026-07-05T02:07:59.426178+00:00"},{"alias_kind":"pith_short_12","alias_value":"UDR2JC66FLEZ","created_at":"2026-07-05T02:07:59.426178+00:00"},{"alias_kind":"pith_short_16","alias_value":"UDR2JC66FLEZAW6E","created_at":"2026-07-05T02:07:59.426178+00:00"},{"alias_kind":"pith_short_8","alias_value":"UDR2JC66","created_at":"2026-07-05T02:07:59.426178+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.07915","citing_title":"CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07057","citing_title":"Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure","ref_index":68,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03045","citing_title":"TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations","ref_index":291,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS","json":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS.json","graph_json":"https://pith.science/api/pith-number/UDR2JC66FLEZAW6EF6J3PEDDGS/graph.json","events_json":"https://pith.science/api/pith-number/UDR2JC66FLEZAW6EF6J3PEDDGS/events.json","paper":"https://pith.science/paper/UDR2JC66"},"agent_actions":{"view_html":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS","download_json":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS.json","view_paper":"https://pith.science/paper/UDR2JC66","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.07600&json=true","fetch_graph":"https://pith.science/api/pith-number/UDR2JC66FLEZAW6EF6J3PEDDGS/graph.json","fetch_events":"https://pith.science/api/pith-number/UDR2JC66FLEZAW6EF6J3PEDDGS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS/action/storage_attestation","attest_author":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS/action/author_attestation","sign_citation":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS/action/citation_signature","submit_replication":"https://pith.science/pith/UDR2JC66FLEZAW6EF6J3PEDDGS/action/replication_record"}},"created_at":"2026-07-05T02:07:59.426178+00:00","updated_at":"2026-07-05T02:07:59.426178+00:00"}