{"as_of":"2026-08-07T21:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:782faf07b3fc72cdf702bdf11c41c67eaab4756c4c1ee17401ae73b7151fa386","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":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":12,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:11:53.731974Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":36,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-07T13:11:53.731974Z","title":"Applied causal inference powered by ml and ai","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.22632","last_updated":"2025-05-28T17:50:20Z","snapshot_observed_at":"2026-08-07T13:00:18.327197Z","submitted_at":"2025-05-28T17:50:20Z","title":"Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.731974Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:b7b03468689b40bbe1ed41c9ba1abbaf03c9a69a1e27429d374a60d0de9bec1f","observation_id":"beda7c65-e6dc-41c7-8c8b-25c713606a08","resolution":{"observed_at":"2026-08-07T13:11:53.731974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-07T01:08:43.632207Z","title":", Hansen, C","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.11960","last_updated":"2025-06-13T17:11:14Z","snapshot_observed_at":"2026-08-07T00:57:35.444003Z","submitted_at":"2025-06-13T17:11:14Z","title":"Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T01:08:43.632207Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2506.11960"},"observation_digest":"sha256:8d74c1f5745e4a54348c3b2055361b8ef7f9e13feba7b6dad8b97e4b1ca4f1ef","observation_id":"8508c037-af27-41ce-8a36-87b6559757b6","resolution":{"observed_at":"2026-08-07T01:08:43.632207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-06T22:13:05.100502Z","title":"Applied causal inference powered by ml and ai","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.22536","last_updated":"2025-06-27T17:15:57Z","snapshot_observed_at":"2026-08-06T22:01:55.620329Z","submitted_at":"2025-06-27T17:15:57Z","title":"Strategic A/B testing via Maximum Probability-driven Two-armed Bandit","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:13:05.100502Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2506.22536"},"observation_digest":"sha256:e824a4c8dc812c625f7b942c135ce603ab33a7cea689736c1cf7e586221106cd","observation_id":"b202ec03-6013-48f3-82ce-ab1b7cd9e2e2","resolution":{"observed_at":"2026-08-06T22:13:05.100502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-06T21:05:38.077600Z","title":"Hansen, N","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01202","last_updated":"2025-07-01T21:44:30Z","snapshot_observed_at":"2026-08-06T20:55:04.422585Z","submitted_at":"2025-07-01T21:44:30Z","title":"Shrinkage-Based Regressions with Many Related Treatments","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:05:38.077600Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2507.01202"},"observation_digest":"sha256:c5c4a280fa68869b1a8f338edf065b9344f17ce7e644dc1d8abe849fa781e12f","observation_id":"54898870-20c7-45e2-99b9-ea37453a2a9b","resolution":{"observed_at":"2026-08-06T21:05:38.077600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-06T17:13:53.451469Z","title":"Applied causal inference powered by ml and ai.arXiv preprint arXiv:2403.02467, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11381","last_updated":"2025-07-16T10:38:29Z","snapshot_observed_at":"2026-08-06T17:06:49.161861Z","submitted_at":"2025-07-15T14:50:41Z","title":"From Observational Data to Clinical Recommendations: A Causal Framework for Estimating Patient-level Treatment Effects and Learning Policies","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:13:53.451469Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2507.11381"},"observation_digest":"sha256:2e1ae988a0583f003c8f9acf0a254289d9685ac4cdc064ca602623859d747543","observation_id":"f20e373d-8109-4403-a6fe-551e15de660a","resolution":{"observed_at":"2026-08-06T17:13:53.451469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-06T15:33:58.591873Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.15909","last_updated":"2025-07-21T15:46:20Z","snapshot_observed_at":"2026-08-07T16:54:07.118168Z","submitted_at":"2025-07-21T15:46:20Z","title":"Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T15:33:58.591873Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2507.15909"},"observation_digest":"sha256:0bf8c69e1f0286d110569cdd9c77a9e8823890b0d127419b4fbd96fdfe903fd5","observation_id":"06734259-93aa-4560-9331-850988fccbb4","resolution":{"observed_at":"2026-08-06T15:33:58.591873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-03T02:51:56.828265Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.09572","last_updated":"2026-07-24T09:08:00Z","snapshot_observed_at":"2026-08-06T11:11:07.339847Z","submitted_at":"2026-02-10T09:22:17Z","title":"Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T02:51:56.828265Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2602.09572"},"observation_digest":"sha256:2024a5b531cbd89c692bcd8b43fe30185e21b769f420823add014500b80782a1","observation_id":"6593ce65-d446-4097-a382-0e457d057831","resolution":{"observed_at":"2026-08-03T02:51:56.828265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":"2403.02467","doi":"10.48550/arxiv.2403.02467","metadata_source":"pith","pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Applied causal inference powered by ML and AI","venue":"econ.EM","work_id":"5282d19c-4a82-45ab-8fea-522709da8b09","year":2024},"citing_paper":{"arxiv_id":"2602.09969","last_updated":"2026-05-14T03:09:09Z","snapshot_observed_at":"2026-08-06T08:58:33.647066Z","submitted_at":"2026-02-10T16:58:50Z","title":"Causal Multi-Task Demand Learning","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-16T02:23:37.433336Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2602.09969"},"observation_digest":"sha256:a75e6f0b52b4357cca69562a9b8cbe0de22aad774d74d96a14cf37ecb502735d","observation_id":"dc80c2ca-058d-4a9b-96e7-0295839cc55e","resolution":{"observed_at":"2026-05-16T02:27:09.779587Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":"2403.02467","doi":"10.48550/arxiv.2403.02467","metadata_source":"pith","pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Applied causal inference powered by ML and AI","venue":"econ.EM","work_id":"5282d19c-4a82-45ab-8fea-522709da8b09","year":2024},"citing_paper":{"arxiv_id":"2605.06386","last_updated":"2026-05-07T15:02:47Z","snapshot_observed_at":"2026-08-02T07:57:34.158260Z","submitted_at":"2026-05-07T15:02:47Z","title":"Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-08T03:40:31.516951Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2605.06386"},"observation_digest":"sha256:f532c8f93ac4744231011f77214de0e7d1a9a933dfb1068984473d023584b21b","observation_id":"52b10efb-f21f-49a8-84f1-8fab1d254f94","resolution":{"observed_at":"2026-05-11T21:56:36.406782Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":"2403.02467","doi":"10.48550/arxiv.2403.02467","metadata_source":"pith","pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Applied causal inference powered by ML and AI","venue":"econ.EM","work_id":"5282d19c-4a82-45ab-8fea-522709da8b09","year":2024},"citing_paper":{"arxiv_id":"2606.21773","last_updated":"2026-06-19T21:45:40Z","snapshot_observed_at":"2026-07-06T23:56:45.447705Z","submitted_at":"2026-06-19T21:45:40Z","title":"Decision-Focused Learning: When and Why Traditional Prediction Models Fail","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T14:24:06.504778Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2606.21773"},"observation_digest":"sha256:bdbd2552bce1fd01a76fa7999f1fc96a4152515315ca8cc8d3958e13ff2f6892","observation_id":"fd41b5f9-a533-43f6-8cfb-637647ee6fdf","resolution":{"observed_at":"2026-07-04T06:29:38.081710Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":"2403.02467","doi":"10.48550/arxiv.2403.02467","metadata_source":"pith","pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Applied causal inference powered by ML and AI","venue":"econ.EM","work_id":"5282d19c-4a82-45ab-8fea-522709da8b09","year":2024},"citing_paper":{"arxiv_id":"2607.01717","last_updated":"2026-07-02T05:15:03Z","snapshot_observed_at":"2026-08-06T23:22:51.206344Z","submitted_at":"2026-07-02T05:15:03Z","title":"From Subgroups to Population Composition: A Transportability Approach to Effect Heterogeneity","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-03T08:21:45.179953Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2607.01717"},"observation_digest":"sha256:b0296f6e9f28f369ae45f2b4bcb3a59bb7dfdd74561726e874c7554200a343fb","observation_id":"9b0bbbc8-9dbd-4007-90a3-e20746438b9b","resolution":{"observed_at":"2026-07-03T08:27:48.192018Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI","version":1},"cited_work":{"arxiv_id":"2403.02467","doi":"10.48550/arxiv.2403.02467","metadata_source":"pith","pith_arxiv_id":"2403.02467","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Applied causal inference powered by ML and AI","venue":"econ.EM","work_id":"5282d19c-4a82-45ab-8fea-522709da8b09","year":2024},"citing_paper":{"arxiv_id":"2607.06412","last_updated":"2026-07-07T15:42:31Z","snapshot_observed_at":"2026-08-07T04:01:39.711381Z","submitted_at":"2026-07-07T15:42:31Z","title":"A Machine-Learning-Compatible Omnibus Test for Treatment Effect Heterogeneity","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-07-08T06:51:22.451502Z"},"links":{"cited_paper":"/paper/2403.02467","citing_paper":"/paper/2607.06412"},"observation_digest":"sha256:167036779bf448f40e6cd32cab8da3b5fa0ed17f3697df6fc01c0865d02019a2","observation_id":"6fcf8aa0-550a-493b-839e-b9f6ec2bceb6","resolution":{"observed_at":"2026-07-08T06:54:44.650310Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.02467/citation-record","integrity":"/paper/2403.02467/integrity","json":"/paper/2403.02467/citation-record.json","paper":"/paper/2403.02467"},"outbound":[],"paper":{"arxiv_id":"2403.02467","last_updated":"2024-03-04T20:28:28Z","latest_version":1,"primary_category":"econ.EM","snapshot_observed_at":"2026-08-07T14:11:24.852373Z","submitted_at":"2024-03-04T20:28:28Z","title":"Applied Causal Inference Powered by ML and AI"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2403.02467."}