{"as_of":"2026-08-09T16:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1d71043d5dfef4b9a37ba7860f2d2951f236203027338a6cd0d0ff76076c5bcf","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T10:23:40.906614Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.23741/citation-record","integrity":"/paper/2606.23741/integrity","json":"/paper/2606.23741/citation-record.json","paper":"/paper/2606.23741"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Causation, Prediction, and Search","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:fe77fcd47a9625e64b8e20e5e3c9ef53061115e01c6b98b36d7ccb796f6a5116","observation_id":"affc2457-0dc5-4c8d-a9c5-0873e4cbeea7","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Advances and open problems in federated learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:eedda8cf645979a8e0d61d89cd2b89f5d3cd41ec2887cac452aa16fd8426451e","observation_id":"f19d5b6c-a5ad-4e01-9d82-ca05dfbf0ab0","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Learning Bayesian network structure from distributed homogeneous data","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:e5b88d28636de9c374562a5433d5e77ada06bdfdf3c6297b3c51ac66dde342e0","observation_id":"c753c282-030e-4a96-bdab-4588a2022099","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Federated causal discovery in medicine: Trends, opportunities, and challenges","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:c4a444342bc2d5666b423db98e383d17ce168b3534f87d9c2705882feb07d41b","observation_id":"60e483e5-2d54-47bb-8f18-eac3067da4e1","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Learning high-dimensional directed acyclic graphs with latent and selection variables","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:781384ea9996dbe2d32779fde41696c26f4ea158c1d245c6f87a0122c04034be","observation_id":"e70202a8-9111-42f8-944a-e9c1d1426869","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Gradient-based neural DAG learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:065ab1abbe56f9abf392ebd3d9af55c9510dd377ff34618210a344513596afc3","observation_id":"268d2d7a-6e93-4215-a302-f50f842ef5ae","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Neural Granger causality","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:2caaa019842b74c1c7f47d6d2d3d035b2df7d7958fde5d9ef9786beecd36fae6","observation_id":"907f9dd2-dc32-4eed-8d16-51829932ac68","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"A generalization of sampling without replacement from a finite universe","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:4a1c861ff3f3d53c4d47992d427b3374498d6bdab79ceca3cd7615d60998c7d6","observation_id":"36e87ff6-d2d1-4079-862f-3c5f37871a9b","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:a3b14677f16357aa5a953ee53d0076a495745a531d01032be2f543593b72e819","observation_id":"7914e3ad-99a9-432f-9a12-48d50d24fa32","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Communication-efficient federated learning via knowledge distillation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:570dd21931cdb4d34408ba1e4c7881f70cb297d9931e39f422e616a01c6490c2","observation_id":"1defda07-210a-4dc8-8664-e3751fa1f076","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Crypten: Secure multi-party computation meets machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:440d8e5e19b27b5838989fe2f46b913eab2738264e84466f769f7397d51d3a90","observation_id":"f71b31ad-1b6a-42ee-8450-5b5a64fa7e64","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"A fully homomorphic encryption scheme","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:8a1c6fdd08ab1437c0efafbd911f937cededa5468478566e7a7f0c0d666d4945","observation_id":"dbea7635-1144-46d3-8c2c-8f4acd81f548","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2603.05149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T09:09:43.688705Z","title":"Federated causal discovery from heterogeneous data","venue":null,"work_id":"920e537f-a93f-4e85-81eb-bffad7ce6350","year":2024},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:19fafb9aefff2a48db835e38027b945c5fcd8a91bfe72dddd571294d4cc3924e","observation_id":"bdc76153-27a2-43f5-9ed7-8161f1caf724","resolution":{"observed_at":"2026-07-04T09:09:43.690363Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Distributed Bayesian network structure learning","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:9bcfac2cbf641f92d15d1fec14307727c2f1de20383be451ff849ab762e46427","observation_id":"dd3d42f2-8684-46c2-a0f4-63374d40f45a","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Federated learning of generalized linear causal networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:2f1eb8ca7ad2ba3e11859428746e8fea81f2753dc1320d0310346958580101fe","observation_id":"c035e0c0-1d67-4cf2-b9fc-6f7c1cb4bf50","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"FedDAG: Federated DAG structure learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:fbe6240524cece26efc148b837ef527b0914df3ce62cb3193818a862772d5059","observation_id":"c88171aa-4830-4c42-9c6d-403cfee1d7f7","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Interventional causal structure discovery over graphical models with convergence and optimality guarantees","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:cb6d22ad16d9fe3547382a7243c1cde9ae69e311d057e86ffd52d09189b99f3e","observation_id":"b23ad3c5-ef37-4033-96e8-d73356672170","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Federated local causal structure learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:0f50bb290ec0ac463e628de22f55eb2d580437c925ec887acf1b3845fb019c8b","observation_id":"62502456-3799-4457-9458-2d30d0180147","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Federated causal structure learning with missing data","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:7af68d6d7b887cb5d3a594a4b3f3e100fefd9aabd2c41e3b18623843bc9e3b41","observation_id":"5af72f1c-52f7-4c5e-81a8-b09486f54838","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Federated multi-task Bayesian network learning in the presence of overlapping and distinct variables","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:baab9eac38f33d5d6abdc0f7be1c2d91779a701e5832ce198912876496a92312","observation_id":"aa95605c-956c-41b4-94b8-4f288508b6e2","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.13004","last_updated":"2026-05-11T19:11:15Z","snapshot_observed_at":"2026-07-06T22:45:49.118915Z","submitted_at":"2026-02-13T15:12:18Z","title":"Towards Uncertainty-Aware Federated Granger Causal Learning","version":2},"cited_work":{"arxiv_id":"2602.13004","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.13004","snapshot_observed_at":"2026-07-04T09:09:43.694184Z","title":"Towards Uncertainty-Aware Federated Granger Causal Learning","venue":"cs.LG","work_id":"10bfb0ad-6f4e-447d-b394-81eee2231d70","year":2026},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"cited_paper":"/paper/2602.13004","citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:3380a9494fa449b81878b86bb688409a9415eac19073c725b82f992733f6aeff","observation_id":"802c035d-8917-4391-ae8f-45adf33ec3c2","resolution":{"observed_at":"2026-07-04T09:09:43.695469Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"An adaptive kernel approach to federated learning of heterogeneous causal effects","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:555e89f4bb0247afb5a72fa8058638584858c769474e7d2e056b231218ca702c","observation_id":"447f12f3-fc7f-4d81-9418-6a82e3917bec","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Federated causal inference in heterogeneous observational data","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:886cbc294a9bf2de5297fbab6ed1e28400f1612c7bb9405e0e8c0e1cbdac4e53","observation_id":"8588b434-0d07-4f9d-a586-ec5a3ec3f0cc","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17705","last_updated":"2024-06-24T04:21:33Z","snapshot_observed_at":"2026-07-30T18:51:47.478923Z","submitted_at":"2024-02-27T17:33:23Z","title":"Federated Learning for Estimating Heterogeneous Treatment Effects","version":2},"cited_work":{"arxiv_id":"2402.17705","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17705","snapshot_observed_at":"2026-07-04T09:09:43.696726Z","title":"Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies","venue":null,"work_id":"7f017e0c-9ef6-4c83-adc0-30fcb293fe00","year":2024},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"cited_paper":"/paper/2402.17705","citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:5b33f0947c8bbba30fc286de313f5549974106393f568b1df670cad134b5487e","observation_id":"da40b0f7-ced0-46ed-a982-fe3df89455e8","resolution":{"observed_at":"2026-07-04T09:09:43.698439Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.16317","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T09:09:43.694775Z","title":"and Yang, S","venue":null,"work_id":"26f93e87-ab7e-4d96-bb42-fa03529e4d4a","year":2025},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:a899fbc52b472c0957ee50171b9696a5fb2c0fc01d3cdc295ab4294919639f6f","observation_id":"3a92df9a-ce99-49ed-94a9-51b190e03386","resolution":{"observed_at":"2026-07-04T09:09:43.696236Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Toward causal representation learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:945db9ebaa763889b2e1079d1fce17ce95f8d9bb36b83124c0a4ae9cac72204c","observation_id":"5998fd84-736a-48e2-be04-a993164fb8f6","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Dense: Data-free one-shot federated learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:3215e111c40e7ff94c7ad4c963d5994b00cf848f1dad5aef9a8c40b5d02452c3","observation_id":"8b3592bd-0773-4c74-a6d2-a811d6e02bbf","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.16902","last_updated":"2025-08-26T11:41:14Z","snapshot_observed_at":"2026-07-06T15:48:17.327427Z","submitted_at":"2023-06-29T12:48:00Z","title":"Integrating Large Language Model for Improved Causal Discovery","version":2},"cited_work":{"arxiv_id":"2306.16902","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.16902","snapshot_observed_at":"2026-07-04T09:09:43.691484Z","title":"From query tools to causal architects: Harnessing large language models for advanced causal discovery from data","venue":null,"work_id":"81c7c884-7770-4c0f-98f2-6c07da78095c","year":2024},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"cited_paper":"/paper/2306.16902","citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:bea74045ef015dd4001c08bb413ba1f2ea2ba3b68fd323a8ebddbbffb094ed04","observation_id":"33f9fd47-bd94-48dc-9774-d4b2b4377ce2","resolution":{"observed_at":"2026-07-04T09:09:43.693052Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T10:23:40.906614Z","title":"Causality-based feature selection: Methods and evaluations","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T10:23:40.906614Z"},"links":{"citing_paper":"/paper/2606.23741"},"observation_digest":"sha256:7870b415973cdc774bc9ae498afe43013f0f9a7157bbe0703c81cb230b4d7e68","observation_id":"26b62b78-ad8e-4130-8dce-07e7a383f9fc","resolution":{"observed_at":"2026-06-26T10:23:40.906614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.23741","last_updated":"2026-06-21T15:44:39Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T18:39:40.613455Z","submitted_at":"2026-06-21T15:44:39Z","title":"A Survey on Federated Causal Discovery and Inference"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":24,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":29},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2606.23741."}