{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:W7F3KAOHON6F3NHJKHHCHQYWJT","short_pith_number":"pith:W7F3KAOH","schema_version":"1.0","canonical_sha256":"b7cbb501c7737c5db4e951ce23c3164cc250ce6588869dc814c297465c44558a","source":{"kind":"arxiv","id":"2410.07013","version":1},"attestation_state":"computed","paper":{"title":"Causal Representation Learning in Temporal Data via Single-Parent Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexandre Drouin, David Rolnick, Dhanya Sridhar, Jakob Runge, Julia Kaltenborn, Peer Nowack, Philippe Brouillard, S\\'ebastien Lachapelle, Yaniv Gurwicz","submitted_at":"2024-10-09T15:57:50Z","abstract_excerpt":"Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Ni\\~no, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\\~no phenomenon and other processes, as well as the causal model over them. The challenge is th"},"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":"2410.07013","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-09T15:57:50Z","cross_cats_sorted":[],"title_canon_sha256":"19493f4487ac654034412739dfac72e7bcd4a7fbecf854b46cd2758c488733fa","abstract_canon_sha256":"984877b22525e0a7a97528b763f893432f2ef3eb63cd2f77993ab90968118523"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:18:09.697074Z","signature_b64":"FQk9vxqW77MLFLd4LdwzyoX9LV4uBOJSClC+BL3/wkFZ3BlttdpDtrs0rg/ia7xFnyKwkPEZqPNcf+Ez5mKZDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7cbb501c7737c5db4e951ce23c3164cc250ce6588869dc814c297465c44558a","last_reissued_at":"2026-07-05T09:18:09.696619Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:18:09.696619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Causal Representation Learning in Temporal Data via Single-Parent Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexandre Drouin, David Rolnick, Dhanya Sridhar, Jakob Runge, Julia Kaltenborn, Peer Nowack, Philippe Brouillard, S\\'ebastien Lachapelle, Yaniv Gurwicz","submitted_at":"2024-10-09T15:57:50Z","abstract_excerpt":"Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Ni\\~no, affect other climate processes at remote locations across the globe. However, scientists typically collect low-level measurements, such as geographically distributed temperature readings. From these, one needs to learn both a mapping to causally-relevant latent variables, such as a high-level representation of the El Ni\\~no phenomenon and other processes, as well as the causal model over them. The challenge is th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.07013","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/2410.07013/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":"2410.07013","created_at":"2026-07-05T09:18:09.696692+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.07013v1","created_at":"2026-07-05T09:18:09.696692+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.07013","created_at":"2026-07-05T09:18:09.696692+00:00"},{"alias_kind":"pith_short_12","alias_value":"W7F3KAOHON6F","created_at":"2026-07-05T09:18:09.696692+00:00"},{"alias_kind":"pith_short_16","alias_value":"W7F3KAOHON6F3NHJ","created_at":"2026-07-05T09:18:09.696692+00:00"},{"alias_kind":"pith_short_8","alias_value":"W7F3KAOH","created_at":"2026-07-05T09:18:09.696692+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.00398","citing_title":"M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data","ref_index":224,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT","json":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT.json","graph_json":"https://pith.science/api/pith-number/W7F3KAOHON6F3NHJKHHCHQYWJT/graph.json","events_json":"https://pith.science/api/pith-number/W7F3KAOHON6F3NHJKHHCHQYWJT/events.json","paper":"https://pith.science/paper/W7F3KAOH"},"agent_actions":{"view_html":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT","download_json":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT.json","view_paper":"https://pith.science/paper/W7F3KAOH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.07013&json=true","fetch_graph":"https://pith.science/api/pith-number/W7F3KAOHON6F3NHJKHHCHQYWJT/graph.json","fetch_events":"https://pith.science/api/pith-number/W7F3KAOHON6F3NHJKHHCHQYWJT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT/action/storage_attestation","attest_author":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT/action/author_attestation","sign_citation":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT/action/citation_signature","submit_replication":"https://pith.science/pith/W7F3KAOHON6F3NHJKHHCHQYWJT/action/replication_record"}},"created_at":"2026-07-05T09:18:09.696692+00:00","updated_at":"2026-07-05T09:18:09.696692+00:00"}