{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:AYZZGAQZOOWMS6MUEOMLSLOUY2","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0e60db82eab35b84f3560fbbc19e030c89cd242d1d2f4593a3df528789ec4e7e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-09-23T13:51:09Z","title_canon_sha256":"b5dc1b49c410adcdf9d08e4cbba1648bf3e2330f3bc4295e245d55e5233e1fc5"},"schema_version":"1.0","source":{"id":"2309.13378","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.13378","created_at":"2026-07-05T06:53:51Z"},{"alias_kind":"arxiv_version","alias_value":"2309.13378v1","created_at":"2026-07-05T06:53:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.13378","created_at":"2026-07-05T06:53:51Z"},{"alias_kind":"pith_short_12","alias_value":"AYZZGAQZOOWM","created_at":"2026-07-05T06:53:51Z"},{"alias_kind":"pith_short_16","alias_value":"AYZZGAQZOOWMS6MU","created_at":"2026-07-05T06:53:51Z"},{"alias_kind":"pith_short_8","alias_value":"AYZZGAQZ","created_at":"2026-07-05T06:53:51Z"}],"graph_snapshots":[{"event_id":"sha256:ea12c20146849b07279b8390fb204e9fad9d21179f1e07111831f8069b355dd9","target":"graph","created_at":"2026-07-05T06:53:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2309.13378/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular method for STG forecasting, but they often struggle with temporal out-of-distribution (OoD) issues and dynamic spatial causation. In this paper, we propose a novel framework called CaST to tackle these two challenges via causal treatments. Concretely, leveraging a causal lens, we first build a structural causal model to decipher the data generation process of STGs. To handle the temporal OoD issue, we employ the back-door adjustme","authors_text":"Haomin Wen, Kun Wang, Roger Zimmermann, Xu Liu, Yutong Xia, Yuxuan Liang, Zhengyang Zhou","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-09-23T13:51:09Z","title":"Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.13378","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b25d48fa7ddd2ea81514a8c197b5c37dbcd88b3e6f533ee0496a03877d112429","target":"record","created_at":"2026-07-05T06:53:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0e60db82eab35b84f3560fbbc19e030c89cd242d1d2f4593a3df528789ec4e7e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-09-23T13:51:09Z","title_canon_sha256":"b5dc1b49c410adcdf9d08e4cbba1648bf3e2330f3bc4295e245d55e5233e1fc5"},"schema_version":"1.0","source":{"id":"2309.13378","kind":"arxiv","version":1}},"canonical_sha256":"063393021973acc979942398b92dd4c6830bb0b538174d62ad104f15bbeab759","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"063393021973acc979942398b92dd4c6830bb0b538174d62ad104f15bbeab759","first_computed_at":"2026-07-05T06:53:51.981223Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:53:51.981223Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zBxDUwTQrdsq0Obb6N9Dayjqz5DeTPnQ8gGPnX2rYsNmqFF5NfjtbRDatXI2sIFP35ItXHD7Y38rtginZ/tUBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:53:51.981722Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.13378","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b25d48fa7ddd2ea81514a8c197b5c37dbcd88b3e6f533ee0496a03877d112429","sha256:ea12c20146849b07279b8390fb204e9fad9d21179f1e07111831f8069b355dd9"],"state_sha256":"4dffb20ce3111d3666591b321afbe1f92914c49212b42f233bf160571b3170e8"}