{"as_of":"2026-08-19T05:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7edcc93c22d80aea6f50719e26d1db7e93ba1d0f061156ca37f220eafa634b1a","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:29:14.901794Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T17:11:15.394709Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.02852","last_updated":"2020-08-26T17:34:29Z","snapshot_observed_at":"2026-08-18T21:32:05.275672Z","submitted_at":"2020-07-06T16:09:00Z","title":"Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.02852","snapshot_observed_at":"2026-08-11T00:10:25.716721Z","title":"Cross-fitting and averaging for machine learning estimation of heterogeneous treatment effects","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2412.19711","last_updated":"2025-04-13T10:24:57Z","snapshot_observed_at":"2026-08-17T18:09:09.620397Z","submitted_at":"2024-12-27T16:10:03Z","title":"Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T00:10:25.716721Z"},"links":{"cited_paper":"/paper/2007.02852","citing_paper":"/paper/2412.19711"},"observation_digest":"sha256:442a35c4655534df6d474c0ceac438e637a9aa7e021d2a684cd464264b627c9c","observation_id":"f4087438-f820-489a-9d64-116c5e452660","resolution":{"observed_at":"2026-08-11T00:10:25.716721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.02852","last_updated":"2020-08-26T17:34:29Z","snapshot_observed_at":"2026-08-18T21:32:05.275672Z","submitted_at":"2020-07-06T16:09:00Z","title":"Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.02852","snapshot_observed_at":"2026-08-16T04:29:14.901794Z","title":"Cross-fitting and averaging for machine learning estimation of heterogeneous treatment effects","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.01145","last_updated":"2025-05-02T09:44:04Z","snapshot_observed_at":"2026-08-18T16:11:53.051871Z","submitted_at":"2025-05-02T09:44:04Z","title":"Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:29:14.901794Z"},"links":{"cited_paper":"/paper/2007.02852","citing_paper":"/paper/2505.01145"},"observation_digest":"sha256:313726ade88ab3e5e6ff65492702df0553dde23a4c553e7b4fb4ebc85dcfb49a","observation_id":"f66db7ce-b08f-4cba-a3a1-75f628b59e7b","resolution":{"observed_at":"2026-08-16T04:29:14.901794Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.02852","last_updated":"2020-08-26T17:34:29Z","snapshot_observed_at":"2026-08-18T21:32:05.275672Z","submitted_at":"2020-07-06T16:09:00Z","title":"Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects","version":2},"cited_work":{"arxiv_id":"2007.02852","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.02852","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2007.02852 , year=","venue":null,"work_id":"d14c40bc-decd-4c7e-8564-5164d8d7e32b","year":2007},"citing_paper":{"arxiv_id":"2605.03141","last_updated":"2026-05-04T20:30:25Z","snapshot_observed_at":"2026-07-06T23:16:05.372336Z","submitted_at":"2026-05-04T20:30:25Z","title":"In-Sample Evaluation of Subgroups Identified by Generic Machine Learning","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-08T17:49:33.846351Z"},"links":{"cited_paper":"/paper/2007.02852","citing_paper":"/paper/2605.03141"},"observation_digest":"sha256:ad63d21e66fe72a244ae6f17d81d695c34878f23d2277a1eff240f82542087d6","observation_id":"6703496d-c8ea-4a24-88d4-855a4b896fe3","resolution":{"observed_at":"2026-05-11T17:11:15.396929Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.02852","last_updated":"2020-08-26T17:34:29Z","snapshot_observed_at":"2026-08-18T21:32:05.275672Z","submitted_at":"2020-07-06T16:09:00Z","title":"Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.02852","snapshot_observed_at":"2026-08-12T00:57:13.804780Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2608.07841","last_updated":"2026-08-08T00:48:59Z","snapshot_observed_at":"2026-08-18T20:04:42.118034Z","submitted_at":"2026-08-08T00:48:59Z","title":"Debiased Machine Learning for Partially Linear Accelerated Failure Time Models","version":1},"reference_index":249,"source":"arxiv_source","source_observed_at":"2026-08-12T00:57:13.804780Z"},"links":{"cited_paper":"/paper/2007.02852","citing_paper":"/paper/2608.07841"},"observation_digest":"sha256:97ce1fa9e14bbf5a00af285a3b2d14965849110a8950166ca6dc0d05bd629b20","observation_id":"3aa9002c-db98-4e00-bd76-b6fbfc6d5520","resolution":{"observed_at":"2026-08-12T00:57:13.804780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2007.02852/citation-record","integrity":"/paper/2007.02852/integrity","json":"/paper/2007.02852/citation-record.json","paper":"/paper/2007.02852"},"outbound":[],"paper":{"arxiv_id":"2007.02852","last_updated":"2020-08-26T17:34:29Z","latest_version":2,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-18T21:32:05.275672Z","submitted_at":"2020-07-06T16:09:00Z","title":"Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2007.02852."}