{"as_of":"2026-08-08T10:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:70154e78e41326f509ff97d2cbcf5d54e74f0563f7c5f2aba89b72f203d594f9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:40:23.348785Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T14:05:26.828545Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2003.10551","last_updated":"2020-03-23T21:08:51Z","snapshot_observed_at":"2026-07-06T09:06:50.593178Z","submitted_at":"2020-03-23T21:08:51Z","title":"G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10551","snapshot_observed_at":"2026-08-06T22:40:23.348785Z","title":null,"venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.21154","last_updated":"2025-06-26T11:24:46Z","snapshot_observed_at":"2026-08-06T22:30:12.102600Z","submitted_at":"2025-06-26T11:24:46Z","title":"Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T22:40:23.348785Z"},"links":{"cited_paper":"/paper/2003.10551","citing_paper":"/paper/2506.21154"},"observation_digest":"sha256:26ff4bb9ac72a2621c0d64de6fb0a6ea8db96a4e318ad971cc841e03427c90dd","observation_id":"99081e76-afb4-45c5-ac48-fd72c4fff1ba","resolution":{"observed_at":"2026-08-06T22:40:23.348785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10551","last_updated":"2020-03-23T21:08:51Z","snapshot_observed_at":"2026-07-06T09:06:50.593178Z","submitted_at":"2020-03-23T21:08:51Z","title":"G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes","version":1},"cited_work":{"arxiv_id":"2003.10551","doi":null,"metadata_source":"pith","pith_arxiv_id":"2003.10551","snapshot_observed_at":"2026-08-05T14:05:26.828545Z","title":"G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes","venue":"cs.LG","work_id":"b14e923b-a381-44cb-a80c-64e53455735c","year":2020},"citing_paper":{"arxiv_id":"2508.21722","last_updated":"2025-08-29T15:38:27Z","snapshot_observed_at":"2026-08-07T22:38:03.368030Z","submitted_at":"2025-08-29T15:38:27Z","title":"Inferring Effects of Major Events through Discontinuity Forecasting of Population Anxiety","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T14:05:25.686035Z"},"links":{"cited_paper":"/paper/2003.10551","citing_paper":"/paper/2508.21722"},"observation_digest":"sha256:8aeff296b01d287c8794e5d296e024da082d68cb6189ce9a00b171778c9b3895","observation_id":"dd10ea4c-8971-4887-8c79-10fad3e3dd4a","resolution":{"observed_at":"2026-08-05T14:05:26.834420Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2003.10551/citation-record","integrity":"/paper/2003.10551/integrity","json":"/paper/2003.10551/citation-record.json","paper":"/paper/2003.10551"},"outbound":[],"paper":{"arxiv_id":"2003.10551","last_updated":"2020-03-23T21:08:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T09:06:50.593178Z","submitted_at":"2020-03-23T21:08:51Z","title":"G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2003.10551."}