{"as_of":"2026-08-12T05:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a512246cf0cdb97578ba7725ce521d70575d278f758e312f77aeba77842f01a","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T03:11:25.821704Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+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/2606.26818/citation-record","integrity":"/paper/2606.26818/integrity","json":"/paper/2606.26818/citation-record.json","paper":"/paper/2606.26818"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T03:11:25.821704Z","title":"Journal of health economics31(1), 219–230 (2012)","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:598ba9e4ee882ad072ede8b7d0ab49d7dbbd7e33033298983c176252b1329ea9","observation_id":"626e63b5-5c35-4d24-8df3-fe52def82592","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Journal of artificial intelligence research4, 129–145 (1996)","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:787e74e29dee046e94b167eecddf81e2f776c7b3b1010b0ca3d8782161d2acc3","observation_id":"e3d2c8ac-3a3d-4571-a91b-241d237b7eea","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"In: The 50th anniversary of Gröbner bases, vol","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:94cd57471d30773460904ce394c36c869ced980e5f32f2620e92541441a080ec","observation_id":"436c75dc-1e58-4274-a299-e8284981e65a","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"10.1201/9781315136288","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Routledge (Sep 2017)","venue":null,"work_id":"16fa8c1e-a7f2-479a-aba6-c468e4742fd9","year":2017},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:65b2deeec1c62aba4a37b1dba82830e3a084f0211af048424fd3749430f55f52","observation_id":"e38f03bc-590a-42f2-b5b0-8d508894194d","resolution":{"observed_at":"2026-06-26T03:18:58.815034Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-06-26T03:11:25.821704Z","title":"In: Causal Learning and Reasoning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:19a144fbf2cfa7d8d576cce5e2388d8c255ad80d58d9d59945e95c577b94b211","observation_id":"ced38d05-c0ed-422a-832d-7ff9351db354","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Journal of the Royal Statistical Society Series B: Statistical Methodology84(2), 579–599 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:38a2b9266acbbc06cfcefb50c6e664867930edca31ef2d88ccb5448710b91d5b","observation_id":"222b4f4b-6735-4043-b645-0e21400aed6a","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"stable/2331932","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T14:39:57.823944Z","title":"Biometrika12(1/2), 134–139 (1918), http://www.jstor.org/stable/2331932","venue":null,"work_id":"46702a50-6c99-4b43-854c-1118d58ab6e0","year":1918},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:64d2229d46200d6d2fcbf6c8dc7cd415ef560597de0be518eb8e294c2f2a8978","observation_id":"db64b2f1-ca54-4519-b0d9-247bfb9e3756","resolution":{"observed_at":"2026-07-04T14:39:57.825473Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+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":"2026-06-26T03:11:25.821704Z","title":"PhysioNet","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:e6b3ef0dd78df37cd6b529f47f4a73c573541f8faf0bedc79c04d8bd5214cd8a","observation_id":"34f12630-20b4-4868-94da-b322281c6084","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Journal of Machine Learning Research8(3) (2007)","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:cfc20b44ef9d3208edb02987ab54a5d1df2b433282ba83d6d019cefcd7ecb2e8","observation_id":"d221af3f-5602-400c-b1d2-cf9211269bec","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Advances in Neural Information Processing Systems33, 20108– 20119 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:f7e21e53f06335ca42fe0e4722da24be856418cde57de317dcfbb7661c4d1613","observation_id":"cb5aeb0e-6a65-4c4e-af2e-463065d9bd67","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Statistics in medicine24(10), 1455–1481 (2005)","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:cccd5de3d6b923abc4ac25b98f65101481ff142e990c80454ceefef5dbd12623","observation_id":"7483800a-7c2b-45a0-928d-f7bb95141f0d","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"BMC medicine16, 1–15 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:f12b15f04a23ad7c4d48e1aa032318e730ec3762f5c7bba1c2b7e858129cd62f","observation_id":"12fc0f17-2b0e-461b-b046-c55971929677","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Machine learning54, 153–178 (2004) Optimizing Experimental Design 9","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:1726c2252b35dc7aba49dfc9a1ceb88dfc2994c6c037698e0ed9aadd2ab62be3","observation_id":"8112105f-b9ac-4067-a678-2c38991a48f3","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":null,"venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:c495b511aa16ee44291aba537fd04c4546c14b4f083348f5044d29f0630603ff","observation_id":"038b64da-8937-4318-9456-9dba32d2f810","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Transport Research Laboratory Crowthorne (2000)","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:e6735688ef4b1ab922cc0aa95a03c3d72ef662eb44f92f8618a00614af7c2bb9","observation_id":"8184f2a2-2796-46d2-933b-e7c54bbbedfb","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","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":"2026-06-26T03:11:25.821704Z","title":"Biometrika94(1), 19–35 (2007) 8 Appendix A: Proofs and Derivations In this appendix, we collect the proofs from the main document","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T03:11:25.821704Z"},"links":{"citing_paper":"/paper/2606.26818"},"observation_digest":"sha256:9e128428fee619993df38c651419e556be5e40e0ddc29f02810ea58947273139","observation_id":"69d6d005-5de4-4e5a-90fd-a6654d7925d2","resolution":{"observed_at":"2026-06-26T03:11:25.821704Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.26818","last_updated":"2026-06-25T10:00:43Z","latest_version":1,"primary_category":"stat.ME","snapshot_observed_at":"2026-07-07T00:01:05.946912Z","submitted_at":"2026-06-25T10:00:43Z","title":"Optimizing Experimental Design for Causal Effect Estimation with Partial Measurements"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":14,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":16},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2606.26818."}