{"as_of":"2026-08-08T14:39:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a71f60fad0b9790237ea6a5ca4da70ec4f99ff811a4fcacb5e02b0af831c45a2","coverage":[{"denominator":86,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":86,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:12:00.270202Z","state":"measured"},{"denominator":87,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":87,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T14:44:24.009878Z","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":[{"citation":{"cited_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},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22632","snapshot_observed_at":"2026-08-04T14:44:24.009878Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.24158","last_updated":"2026-07-27T15:40:07Z","snapshot_observed_at":"2026-08-06T18:53:10.430019Z","submitted_at":"2025-09-29T01:17:28Z","title":"Towards Efficient Inference under Nonmonotone Missingness with General Imputation","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-04T14:44:24.009878Z"},"links":{"cited_paper":"/paper/2505.22632","citing_paper":"/paper/2509.24158"},"observation_digest":"sha256:6ccabd37917918f71cada321255acad0a0a5b004148e11fa467fce7f2e632e78","observation_id":"a762a85b-f33f-469d-8693-16ab28ad087f","resolution":{"observed_at":"2026-08-04T14:44:24.009878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.22632/citation-record","integrity":"/paper/2505.22632/integrity","json":"/paper/2505.22632/citation-record.json","paper":"/paper/2505.22632"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:11:51.599043Z","title":"write newline","venue":null,"work_id":null,"year":null},"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":1,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:51.599043Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:23388747490ef8e95f3d0507b85fc89a64d5321a12adf350ec014a38971554e7","observation_id":"1475343d-bfe9-43df-826d-21e18b4c90cb","resolution":{"observed_at":"2026-08-07T13:11:51.599043Z","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":"2026-08-07T13:11:51.668916Z","title":"M., Toni, L., and Rodrigues, M","venue":null,"work_id":null,"year":2022},"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":2,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:51.668916Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:0f7b47377a59183cab4fec036eafd13bc417ef3d9c433ab11a9a9849637e94c5","observation_id":"2ece043e-8d7a-46ad-bf76-eefb7a0993aa","resolution":{"observed_at":"2026-08-07T13:11:51.668916Z","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":"2026-08-07T13:11:51.809695Z","title":"N., Bates, S., Fannjiang, C., Jordan, M","venue":null,"work_id":null,"year":2023},"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":3,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:51.809695Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:e4bd368cf1e473409ad1bf91b24ea5168416a94144baea7997d96229fa56bca5","observation_id":"975b85bc-0b90-42ae-8eef-a88de832f0f0","resolution":{"observed_at":"2026-08-07T13:11:51.809695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01453","last_updated":"2024-03-26T01:44:52Z","snapshot_observed_at":"2026-07-06T16:42:19.938941Z","submitted_at":"2023-11-02T17:59:04Z","title":"PPI++: Efficient Prediction-Powered Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01453","snapshot_observed_at":"2026-08-07T13:11:51.933057Z","title":"N., Duchi, J","venue":null,"work_id":null,"year":2023},"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":4,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:51.933057Z"},"links":{"cited_paper":"/paper/2311.01453","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:8bd9003772ebcae6dacb9e0e33e6c3e668fc9647b5e2728c4a496878a91f290f","observation_id":"35f54098-aba3-4eeb-9576-e96ffec83be7","resolution":{"observed_at":"2026-08-07T13:11:51.933057Z","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":"2026-08-07T13:11:52.052758Z","title":"W., and Kang, H","venue":null,"work_id":null,"year":2019},"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":5,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.052758Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:88a2919982e03f0bac62b6b2643756088efe2342fbc1250dbd2df9ce14d7ee9d","observation_id":"92d9ddc8-cfc9-4b20-a9b7-07ceefed3017","resolution":{"observed_at":"2026-08-07T13:11:52.052758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.09676","last_updated":"2025-05-28T16:04:08Z","snapshot_observed_at":"2026-07-06T09:29:54.899176Z","submitted_at":"2020-06-17T06:30:48Z","title":"Using Experiments to Correct for Selection in Observational Studies","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.09676","snapshot_observed_at":"2026-08-07T13:11:52.189456Z","title":"Combining experimental and observational data to estimate treatment effects on long term outcomes","venue":null,"work_id":null,"year":2006},"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":6,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.189456Z"},"links":{"cited_paper":"/paper/2006.09676","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:665cf59015705c47b60932709ef32e3147a651b004d59183b23ee94e054159d3","observation_id":"e963d633-d78d-41c6-b988-2a377213ae82","resolution":{"observed_at":"2026-08-07T13:11:52.189456Z","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":"2026-08-07T13:11:52.327974Z","title":"D., Sklar, M., Berk, R., Buja, A., and Zhao, L","venue":null,"work_id":null,"year":2022},"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":7,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.327974Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:be2fbd798d2794aede6e183039c6b85f229336e71d2256eb0fa397b385848b1d","observation_id":"8bed01a9-d04e-484c-a86d-625b57142539","resolution":{"observed_at":"2026-08-07T13:11:52.327974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1305.2019","last_updated":"2013-05-09T07:14:38Z","snapshot_observed_at":"2026-07-06T03:13:00.659828Z","submitted_at":"2013-05-09T07:14:38Z","title":"Dual and anti-dual modes in dielectric spheres","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1305.2019","snapshot_observed_at":"2026-08-07T13:11:52.465101Z","title":"Assumption lean regression","venue":null,"work_id":null,"year":2019},"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":8,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.465101Z"},"links":{"cited_paper":"/paper/1305.2019","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:50e8c48ae2cae42fa9b56cc2ae7b5533224915ce7fd4f51c5bb63fc7b5577821","observation_id":"e7fcc4a1-5426-402f-a508-8e30bb8c561a","resolution":{"observed_at":"2026-08-07T13:11:52.465101Z","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":"2026-08-07T13:11:52.618110Z","title":"J., Klaassen, J., Ritov, Y., and Wellner, J","venue":null,"work_id":null,"year":1993},"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":9,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.618110Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:e2a1b782bc71f92ff933bdaf650c96e7879dcddbcbee9c632c86b094ae0fac33","observation_id":"24e4ab99-7c38-4263-9d41-381e27157967","resolution":{"observed_at":"2026-08-07T13:11:52.618110Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:14.195771Z","title":"Models as approximations i: consequences illustrated with linear regression","venue":null,"work_id":"53634407-8f8b-4ecd-9683-39c6b6e21876","year":2019},"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":10,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.758774Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:736c827dada2809cc59ce527c199c9df114fba50bb7c9bddafdeef955dbb1529","observation_id":"f3228a0a-5539-45a7-8b6e-f64c2c9ab32b","resolution":{"observed_at":"2026-08-07T13:12:14.230889Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:14.082901Z","title":null,"venue":null,"work_id":"8edcc3d9-3da7-4ebb-9188-098768832d2b","year":2010},"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":11,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.880930Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:68650da5793741443ff63975e784f7666f09363f882abc77eb5315b8abfc7f4e","observation_id":"d4329776-970d-4afd-89f1-2a350acabb20","resolution":{"observed_at":"2026-08-07T13:12:14.139846Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:13.885021Z","title":"and Guo, Z","venue":null,"work_id":"9b2389d3-7b4e-43a4-87b9-40dbac31769e","year":2020},"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":12,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:52.999896Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:c9859e07bd9cff22372f129357631e8bd0a17f95b3862819739f621bb1cfb8d8","observation_id":"87f47f8b-20f7-431e-aa36-add0db7eda43","resolution":{"observed_at":"2026-08-07T13:12:13.993758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:13.692304Z","title":"J., Ruppert, D., Stefanski, L","venue":null,"work_id":"c3f5bcb0-3906-419f-95d2-446a70a442aa","year":2006},"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":13,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.046028Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:e770ddea9b751dc625d290da9bc5f1c2360317bf273be2e9b31e84b641c25983","observation_id":"5ada0365-bcef-491a-90da-346d4db2a71d","resolution":{"observed_at":"2026-08-07T13:12:13.778258Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:11:53.192758Z","title":"and Cai, T","venue":null,"work_id":null,"year":2018},"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":14,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.192758Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:d5a7f5f9ac55427d5e992b119df534436315380ff0a9328d08ee05626908ff72","observation_id":"671e577f-ef48-49f8-be63-f87d30bbde9d","resolution":{"observed_at":"2026-08-07T13:11:53.192758Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:13.550638Z","title":"Semi-supervised learning","venue":null,"work_id":"e0fc6d95-9a6e-4b32-8755-10c38ebc69ee","year":2009},"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":15,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.284836Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:a2dcf4a79e849277a3d69fcaa3171ebf5bca63b77bd5c7fa18797eee2366e868","observation_id":"99191c8f-7430-4a1e-bf49-3a2aaccdb147","resolution":{"observed_at":"2026-08-07T13:12:13.620526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:13.317105Z","title":"and White, H","venue":null,"work_id":"af90fc42-311e-469d-bd6e-6aa98b1bb9ad","year":1999},"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":16,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.397956Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:e97ae7ac4e8a4de5b635a2cb91a3b0c43d301fb715968d220f147790382f190d","observation_id":"f91dcb25-28f1-49dd-9609-580129631bc7","resolution":{"observed_at":"2026-08-07T13:12:13.422570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:13.182721Z","title":"N., and Cai, T","venue":null,"work_id":"5373d2cc-58ba-4593-964f-fb01ba3cc921","year":2021},"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":17,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.509128Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:9924fd6ba7615a46fc905ed903b582bba82a08cf516c0345f19ee5d4ef57e1c1","observation_id":"3ad30b75-4d93-468c-8003-129d363f62f5","resolution":{"observed_at":"2026-08-07T13:12:13.249424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:13.003847Z","title":"Double/debiased machine learning for treatment and structural parameters: Double/debiased machine learning","venue":null,"work_id":"1f7cea93-ad0b-4b50-a61f-da0bc8adbe70","year":2018},"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":18,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.609716Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:050314fae32aa1368b10b9a252c5507be8fd35d450b75ccf22e434817ed423fe","observation_id":"0c2707b4-9ccb-4eb6-b838-7b7d7e15dad0","resolution":{"observed_at":"2026-08-07T13:12:13.081651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"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":"1306.6078","last_updated":"2013-06-25T20:00:09Z","snapshot_observed_at":"2026-07-06T03:16:45.088192Z","submitted_at":"2013-06-25T20:00:09Z","title":"A Computational Approach to Politeness with Application to Social Factors","version":1},"cited_work":{"arxiv_id":"1306.6078","doi":null,"metadata_source":"pith","pith_arxiv_id":"1306.6078","snapshot_observed_at":"2026-08-07T13:12:01.124826Z","title":"A Computational Approach to Politeness with Application to Social Factors","venue":"cs.CL","work_id":"3aceaf85-f3cc-4fff-a6b2-12decc246d35","year":2013},"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":20,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.831618Z"},"links":{"cited_paper":"/paper/1306.6078","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:95113fe683584c70058af0f555f85ec971b5c1d0295e9ddfb895c57ed3fb285f","observation_id":"aa0eb6d2-9dc9-485f-b493-56ced718f416","resolution":{"observed_at":"2026-08-07T13:12:01.199439Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:12.841862Z","title":"Optimal and safe estimation for high-dimensional semi-supervised learning","venue":null,"work_id":"69c72b75-c023-4a27-919f-02455762b0bd","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":21,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:53.939507Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:d18d4dd2c1c9f55104e099bad8747fdb476d87ba1dd3518eff4bc08401317c17","observation_id":"38d89b49-02f1-4ae5-9e61-0c8bfc431ef3","resolution":{"observed_at":"2026-08-07T13:12:12.916523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:12.696394Z","title":null,"venue":null,"work_id":"022ef165-7ca1-4fee-a585-174b928baf14","year":2014},"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":22,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.051675Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:49de35bca9913d86aa4090b2a51f09786c1d5bad2741e3133a2dff5c4cf72be1","observation_id":"3a194502-10b1-484a-8b4e-1df9cdc8df42","resolution":{"observed_at":"2026-08-07T13:12:12.767222Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:12.517044Z","title":null,"venue":null,"work_id":"282287ce-3173-4841-9b54-ad0e3d696bbd","year":2023},"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":23,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.197855Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:abcabcfef28dc38928da30bc986ba677eecba92faeb4ca00f0aeef9b1c113ee2","observation_id":"0605e5d6-07c8-4308-8b8f-943d53846800","resolution":{"observed_at":"2026-08-07T13:12:12.601417Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:12.370745Z","title":"and Kennedy, E","venue":null,"work_id":"ad4e307b-39ef-4e5b-b7f0-dca12358765a","year":2021},"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":24,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.313687Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:191ad3b60dbb43869e90d5236cbe12f1bcc481bc47e378a019cd149210c43296","observation_id":"4d057ce6-0841-48b8-9b12-697d3c172ed6","resolution":{"observed_at":"2026-08-07T13:12:12.422620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12365","last_updated":"2025-01-21T21:40:40Z","snapshot_observed_at":"2026-08-05T16:24:45.794745Z","submitted_at":"2024-12-16T21:36:11Z","title":"On the Role of Surrogates in Conformal Inference of Individual Causal Effects","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12365","snapshot_observed_at":"2026-08-07T13:11:54.396815Z","title":"B., and Han, L","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":25,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.396815Z"},"links":{"cited_paper":"/paper/2412.12365","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:48ae4197380db8e6710944b210b30a279a97ac1a681d84f69bb2dd446c597a0a","observation_id":"3ff1660f-183f-4b69-bc8c-566cfa1bc561","resolution":{"observed_at":"2026-08-07T13:11:54.396815Z","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":"2026-08-07T13:11:54.495898Z","title":"A unified view of label shift estimation","venue":null,"work_id":null,"year":2020},"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":26,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.495898Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:b034737952543811e9de054f5c4ab88618ee6464be88311c626ba603b079b51f","observation_id":"2f70acdc-a802-4f31-9c4d-547306fe2ad0","resolution":{"observed_at":"2026-08-07T13:11:54.495898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15204","last_updated":"2025-02-08T15:15:35Z","snapshot_observed_at":"2026-07-06T19:06:41.697671Z","submitted_at":"2024-08-27T17:03:18Z","title":"Can Unconfident LLM Annotations Be Used for Confident Conclusions?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.15204","snapshot_observed_at":"2026-08-07T13:11:54.581815Z","title":"J., and Jurafsky, D","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":27,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.581815Z"},"links":{"cited_paper":"/paper/2408.15204","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:74d8fb52032108408c2ee74847b7cf6dff4a93ac9e176cd5ab18f6e57fbb324e","observation_id":"27971498-18c7-40ba-af55-88d9a79a9900","resolution":{"observed_at":"2026-08-07T13:11:54.581815Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:12.205279Z","title":"Covariate shift by kernel mean matching","venue":null,"work_id":"0953b85b-3d61-40e5-840e-95b66e561fde","year":2009},"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":28,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.673497Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:bfa161534e084fe33ba4f583fdab54d86f858df4dbe6771f3aab5491ad7d036f","observation_id":"239f2dd4-dcd8-4f02-8cb8-9e67bd014751","resolution":{"observed_at":"2026-08-07T13:12:12.292547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:11:54.778001Z","title":"R., and Cheng, D","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":29,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.778001Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:482265f90eb7a3adc1cd21da1a0b48221ac669087d47fda09b512baab6ff7896","observation_id":"c160a487-8dfc-42c2-8317-8adc51cf106e","resolution":{"observed_at":"2026-08-07T13:11:54.778001Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.907681Z","title":"Label correction of crowdsourced noisy annotations with an instance-dependent noise transition model","venue":null,"work_id":"fe422d3e-a3b3-4ddb-8514-6baecb79d182","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":30,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.853276Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:e9c4b3aa802202354a5819fa323e8f116a2ccc8d2f175122920d839586fb2f44","observation_id":"c4f32145-080b-48a7-a369-8f2bd2271994","resolution":{"observed_at":"2026-08-07T13:12:12.088096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.672184Z","title":"Top challenges from the first practical online controlled experiments summit","venue":null,"work_id":"9b114054-e6bf-463d-b87d-c3adec9b009c","year":2019},"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":31,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:54.934293Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:4346d00660a0a896ddc6b0ea583f9fd759ed16a2272bd19a23f2fd8cd5a2570f","observation_id":"c977abba-df09-4bf3-9783-73e57a0a95ac","resolution":{"observed_at":"2026-08-07T13:12:11.784811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.446355Z","title":"Demystifying statistical learning based on efficient influence functions","venue":null,"work_id":"4ffdb723-5e56-4fee-9643-341b92a3c4a1","year":2022},"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":32,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.012616Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:5b13184c0ddfc40d9bd6b3d22b0b33a066a2fd4abd46f7292a983764dce4643f","observation_id":"7ce77d2c-7d6f-423c-a23c-b6634d71c5b6","resolution":{"observed_at":"2026-08-07T13:12:11.562102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.275288Z","title":"Surrogate assisted semi-supervised inference for high dimensional risk prediction","venue":null,"work_id":"2b3edc45-9c58-4ca9-9b30-5ae17d13af7d","year":2023},"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":33,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.090319Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:adea3aa54ff9ed084693874b24165e333f8aa843c92db592f0477bb3920bd0d1","observation_id":"54294853-73ad-46f7-ad77-7050143168c2","resolution":{"observed_at":"2026-08-07T13:12:11.342947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:11.075830Z","title":"Correcting sample selection bias by unlabeled data","venue":null,"work_id":"7d8f5e27-402c-4dc0-9035-c68ed6188b2f","year":2006},"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":34,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.181579Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:5810a3e903b558a318609bcf27d426bedeb44e30bcf6d6de6eec88e6fc1b23c5","observation_id":"75fa22b9-51b7-4625-89cb-61387038cb75","resolution":{"observed_at":"2026-08-07T13:12:11.209762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.829272Z","title":"and Newey, W","venue":null,"work_id":"77f2598b-526c-4928-988b-674f054593b8","year":2022},"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":35,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.267892Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:d6486f6433f3fd03be952618e0d73607d276adb29af96e6c151d54031c976c60","observation_id":"7ddefaae-e9aa-4b85-81ce-1ca2cb4d24d6","resolution":{"observed_at":"2026-08-07T13:12:10.957901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.543513Z","title":"Long-term causal inference under persistent confounding via data combination","venue":null,"work_id":"16721423-b589-42c1-b405-a2d1a6ab6de7","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":36,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.319843Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:601c0337aef2fa9d85fd93a5ca43307b3af40be9e916434ae5b52abf8d924062","observation_id":"3b8bd2c0-528d-4c9c-bf1f-e870848708b7","resolution":{"observed_at":"2026-08-07T13:12:10.704321Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.350210Z","title":"Maximum mean discrepancy for class ratio estimation: Convergence bounds and kernel selection","venue":null,"work_id":"595d7dcb-926f-439a-8826-6a2c0b92e515","year":2014},"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":37,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.400529Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:e7fb132355b4e34237cbe815aa98cd73de91a70640c003d7f7d3617ddf4f6e58","observation_id":"95a1f8a2-2bb7-42ac-ba70-92a181e60cdd","resolution":{"observed_at":"2026-08-07T13:12:10.423194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2501.09731","last_updated":"2026-06-21T18:34:43Z","snapshot_observed_at":"2026-07-06T20:22:04.328179Z","submitted_at":"2025-01-16T18:30:33Z","title":"Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.09731","snapshot_observed_at":"2026-08-07T13:11:55.476147Z","title":"Predictions as surrogates: Revisiting surrogate outcomes in the age of ai","venue":null,"work_id":null,"year":2025},"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":38,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.476147Z"},"links":{"cited_paper":"/paper/2501.09731","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:75ba1bb3eeb2408b730c5fdbc74678010e4a0071dc234c3cd72fbdb0b7d1620a","observation_id":"365380fd-5790-44eb-840a-0d7f8d029867","resolution":{"observed_at":"2026-08-07T13:11:55.476147Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:10.132429Z","title":"and Mao, X","venue":null,"work_id":"b76172d5-a591-4e22-b957-8336b81b6b6d","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":39,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.518854Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:5021001d3357b6aa6ad9ce3a5fe5829a9b7041053f6e522f875036ed293f5aeb","observation_id":"8de87481-825d-409d-a554-905f213995a2","resolution":{"observed_at":"2026-08-07T13:12:10.256994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.848309Z","title":null,"venue":null,"work_id":"01a75b3c-f3d3-4e0f-804d-bc9b9a51ccc2","year":2015},"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":40,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.628005Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:bb6c908c7856380630067d982c558977a25da4c29b515bee4ba050a6cb7cbdf8","observation_id":"920fda0a-51ff-4aec-b022-5aeed8e76099","resolution":{"observed_at":"2026-08-07T13:12:09.964110Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.574072Z","title":null,"venue":null,"work_id":"cebdc2ac-6352-485c-b5e7-3b9e35b7a9a9","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":41,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.739091Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:2c2a831301e49fb711074b131a8a1ab4a9505386c021b48e65bf440c68c496fa","observation_id":"081f5286-1e61-4ef8-9743-500010f25f75","resolution":{"observed_at":"2026-08-07T13:12:09.673918Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.335348Z","title":"Retasa: A nonparametric functional estimation approach for addressing continuous target shift","venue":null,"work_id":"cd698229-de41-408f-a8aa-ca7b77ce6d4f","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":42,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.847455Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:6251d93de032d97ce71f272006296434ac26e2b58d51f53f681da820994aa508","observation_id":"ec687a6a-5b4d-4177-85dc-110d9962c940","resolution":{"observed_at":"2026-08-07T13:12:09.449806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:09.101226Z","title":"Semi-supervised regression: A recent review","venue":null,"work_id":"2a4ce5a2-e40c-421e-a222-a77d8bfabfa0","year":2018},"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":43,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:55.957022Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:8bd735319b58acfb3b06e8f037841681e04a44f68702e4c28f4392d9232703c8","observation_id":"bb9106b5-8a47-465e-a432-f668f0911053","resolution":{"observed_at":"2026-08-07T13:12:09.212192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.29459","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:00.798713Z","title":null,"venue":null,"work_id":"659779af-86ce-40aa-9caa-029cb81fe15c","year":2021},"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":44,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.060712Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:46bf094bfde1b6a3f1119f5f88e608ca9d45635a184b3189e0e21b013745f297","observation_id":"e032c77a-aff2-46b4-aa99-cd821c8d4fc7","resolution":{"observed_at":"2026-08-07T13:12:00.875994Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:08.793444Z","title":"and Martinet, G","venue":null,"work_id":"f85440a3-8617-444f-bbfd-99f34a371826","year":2021},"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":45,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.179287Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:2859d2fbb9951f7af727ea34c0a788334888edcb5c193f0ad9a4f2978cb61ba9","observation_id":"6ab92508-ebb1-48e0-8df2-2de738609bdb","resolution":{"observed_at":"2026-08-07T13:12:08.904990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:08.595958Z","title":"and Sch \\\"o lkopf, B","venue":null,"work_id":"85c1808d-9d72-4055-a86a-984a2b50b5e6","year":2001},"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":46,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.279830Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:8a0f1effec391290ab4cf3bdf859a923e9298448dc318363cc73aa6196cbf649","observation_id":"a81566e7-6d15-4f82-9111-27410e5dfc1b","resolution":{"observed_at":"2026-08-07T13:12:08.647943Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:08.481977Z","title":"Doubly flexible estimation under label shift","venue":null,"work_id":"2e4e63db-95ff-4196-b8ef-dfb9fa308c7f","year":2025},"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":47,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.388414Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:755841f34d787f776dde131e012195364d75a24c701d51696b26eb75b8854262","observation_id":"3a40a25b-0c00-4ff1-898d-5fc9aea8e026","resolution":{"observed_at":"2026-08-07T13:12:08.535067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:08.354454Z","title":"T., Bost, S., Lyu, T., Wu, Y., Hogan, W., Prosperi, M., et al","venue":null,"work_id":"a7bf9ad5-ab00-4224-954e-96558876c723","year":2025},"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":48,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.468595Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:bdf07dc6dd9ed303f2ca59dd6833de1ad0ce0fd72ab0afc8227d47ecdf295426","observation_id":"6b1644f6-edc8-41b1-a824-ba1b19e4b325","resolution":{"observed_at":"2026-08-07T13:12:08.419077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:08.248317Z","title":"Provably end-to-end label-noise learning without anchor points","venue":null,"work_id":"2d6105d5-5b7d-433d-82a5-46659aeb3dd8","year":2021},"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":49,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.545683Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:73caf62d9151de64db83c30335e5f782961fcd88a436899c3eeb2ef940e974bb","observation_id":"be0c0b62-992a-442d-aa27-df01d29b8fa5","resolution":{"observed_at":"2026-08-07T13:12:08.299880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:08.065174Z","title":"Detecting and correcting for label shift with black box predictors","venue":null,"work_id":"c21f7955-aa26-4884-83a4-5a183c138f05","year":2018},"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":50,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.627986Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:05f5a5b9a2f51329649c7ffb201b45c57ec05dc39693e193ffc50de8a9656cef","observation_id":"a604752c-90e1-45e7-b011-ee9f47279cff","resolution":{"observed_at":"2026-08-07T13:12:08.143346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:07.887844Z","title":"Identifiability of label noise transition matrix","venue":null,"work_id":"30e73672-c326-44eb-9697-a50182db5a57","year":2023},"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":51,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.740619Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:01191179f2636d81dafdc42ab1eaf3cda073912ebd2efba9b306036910d6b0d9","observation_id":"52a32e7f-8c25-46d1-a188-7492c9cd2a02","resolution":{"observed_at":"2026-08-07T13:12:07.967122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:07.735258Z","title":"Improved estimators for semi-supervised high-dimensional regression model","venue":null,"work_id":"8044ecee-7ab2-453c-92fa-4b5080d6cdc9","year":2022},"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":52,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.863636Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:a4eb51406ef5e6f83843f31251cf110eb72c6831c8dfe044baf15fe1d9a83433","observation_id":"614dcbea-0741-4efb-b275-6aa89148ac30","resolution":{"observed_at":"2026-08-07T13:12:07.810200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.14220","last_updated":"2024-09-16T17:47:54Z","snapshot_observed_at":"2026-07-06T16:51:44.144280Z","submitted_at":"2023-11-23T22:41:30Z","title":"Assumption-Lean and Data-Adaptive Post-Prediction Inference","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.14220","snapshot_observed_at":"2026-08-07T13:11:56.974667Z","title":"Assumption-lean and data-adaptive post-prediction inference","venue":null,"work_id":null,"year":2023},"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":53,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:56.974667Z"},"links":{"cited_paper":"/paper/2311.14220","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:95fff812d4e1c36651378077c9e0c6ceaf7c5b255e34d80f7270ccf6b9c32fb7","observation_id":"1ad91ded-ba46-4338-8fb0-cab244486610","resolution":{"observed_at":"2026-08-07T13:11:56.974667Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:07.571059Z","title":"Valid inference for machine learning-assisted genome-wide association studies","venue":null,"work_id":"90fde25f-9b5b-46fa-a2c7-cb0d97fa386e","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":54,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.074521Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:421eb3dd9e2eb4a25b362aa429f31d629fdb53acafa395aca69a574cf116b1ef","observation_id":"22079857-40d8-4b6f-afca-0cb16b787ff0","resolution":{"observed_at":"2026-08-07T13:12:07.669843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:07.404008Z","title":"and Witten, D","venue":null,"work_id":"aa8ea31f-3a4b-422e-8a3e-d17a710e824f","year":2023},"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":55,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.151461Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:4fff1fc8ba19d18afba506515d9e80a58f3696cf0557c5d1398fd9892659feee","observation_id":"dd92e72c-e0bd-46e4-8723-a36f540b033e","resolution":{"observed_at":"2026-08-07T13:12:07.491522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:07.183411Z","title":"D., Christoffel, M., and Sugiyama, M","venue":null,"work_id":"5cfd9dca-26d1-433c-aff6-bee367a66624","year":2016},"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":56,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.236502Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:56a8c898d45c300b5f8c172e9bc00f25923d57ecc7bee308fb09e97c5c782d9b","observation_id":"cc582403-d2cf-4f1e-876c-1d6df6a76948","resolution":{"observed_at":"2026-08-07T13:12:07.308816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:06.924948Z","title":null,"venue":null,"work_id":"62d8e97e-2e82-4405-b19c-4e43b826104b","year":1989},"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":57,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.336570Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:aa6e67ef7cd781faa343e4fb5d0deab0edcc32d2825ab7e4a27f12dcd9b96f5a","observation_id":"5d86f843-4eec-4c5e-b011-ecc18e1835d9","resolution":{"observed_at":"2026-08-07T13:12:07.038202Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:06.685983Z","title":null,"venue":null,"work_id":"bf8f64c0-012e-46f3-a808-98dec9a71c20","year":2008},"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":58,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.444363Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:8671f09ee41e356fb5117b15de8758c8d630aed29c4e55dac84ce290190689a6","observation_id":"d8af5d15-5e68-44bc-8337-62bcc28a4e28","resolution":{"observed_at":"2026-08-07T13:12:06.773342Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:06.432098Z","title":"Generalization error bounds in semi-supervised classification under the cluster assumption","venue":null,"work_id":"76b46135-b6ef-4972-90f7-702dbde049ce","year":2006},"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":59,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.543176Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:17b4a37808b4c27e2941b6775e3c82772e4df3389811aebf60341afc7e74bdca","observation_id":"19eeec35-9fc3-4bd6-ae83-9a6475439af7","resolution":{"observed_at":"2026-08-07T13:12:06.533732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:06.196730Z","title":"In all likelihoods: Robust selection of pseudo-labeled data","venue":null,"work_id":"7e9b8a7e-4941-451a-a6d0-c1d41862868a","year":2023},"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":60,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.650867Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:8ab47bfeb86559062a84e3c971cda7ec4a3d612b62a4c06d234519747e20f854","observation_id":"6fcddadc-10b5-4191-88aa-47bcfd93c3ad","resolution":{"observed_at":"2026-08-07T13:12:06.305642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:06.012699Z","title":"On causal and anticausal learning","venue":null,"work_id":"c179d4f1-849d-410d-ac82-b9da14afb7d4","year":2012},"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":61,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.761237Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:02a6008f5b9efa64e85c9bdaf4114e9e22d607ff1c48bcc0cdce9769f9800990","observation_id":"bd703f8a-1978-4dd1-8efb-e70464fece93","resolution":{"observed_at":"2026-08-07T13:12:06.138288Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:05.794966Z","title":"A rate of convergence for mixture proportion estimation, with application to learning from noisy labels","venue":null,"work_id":"72f2c6fd-53a3-4781-8c36-934ca5850ef8","year":2015},"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":62,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.870222Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:5a43ed79c372b812902f829899b8e428d921983ef878aa84202f675e8512d79f","observation_id":"0814729a-2a66-4d88-a29d-972de488cc32","resolution":{"observed_at":"2026-08-07T13:12:05.891635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:05.612843Z","title":"Improving predictive inference under covariate shift by weighting the log-likelihood function","venue":null,"work_id":"a76a92bd-c33a-4395-a55a-9c61fe1ca656","year":2000},"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":63,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:57.978732Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:24ef97d022e55c1c2a4f4342484a3543d187d1c99d663159377ca33293807b07","observation_id":"3bf69dd6-fbb8-478c-9d1d-40718a07d29b","resolution":{"observed_at":"2026-08-07T13:12:05.675406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:05.383386Z","title":"A general m-estimation theory in semi-supervised framework","venue":null,"work_id":"c41dd22d-47a9-4203-bcc2-998ca35c0b21","year":2023},"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":64,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.083116Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:d8468f25b59ce3a1dd42bbbc754953d2698c4e7aff69fba97e9e5c4abef8bfc3","observation_id":"be8b39ef-b456-4222-aacc-d3b71057635d","resolution":{"observed_at":"2026-08-07T13:12:05.491797Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:05.141717Z","title":"and Cowie, C","venue":null,"work_id":"75ad8855-4b63-4b2d-b1c5-03d91c89e87e","year":2012},"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":65,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.195803Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:ed37dd8a2bf8c43112053faaca7f696f2b4f5c4382e641e5115001b6146c4935","observation_id":"1ba23859-04a7-4562-b850-3ad7c4cb6bb9","resolution":{"observed_at":"2026-08-07T13:12:05.261376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:04.914014Z","title":"When training and test sets are different: characterizing learning transfer","venue":null,"work_id":"2b47e228-c9c6-45f0-990f-b50c06ce5bf3","year":2009},"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":66,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.277939Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:d6a073e151678e07353a5a0b55fac6898453cd01c8fb4f324fc06e2ed02eb8a4","observation_id":"2dc105a1-ffd7-4ecc-841a-d52144173741","resolution":{"observed_at":"2026-08-07T13:12:05.038205Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:04.695869Z","title":"and Kawanabe, M","venue":null,"work_id":"f73a150b-f958-4d9e-aaac-1139e0e99981","year":2012},"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":67,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.378439Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:6874003d2f005078b83d2d0340591833b1fa6bd7ec20177ef838e1baac0acc3f","observation_id":"212b2692-1181-4fd3-970b-a018c27b2899","resolution":{"observed_at":"2026-08-07T13:12:04.820024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:04.463324Z","title":"Direct importance estimation for covariate shift adaptation","venue":null,"work_id":"bbf7b99f-2d18-4532-8d8d-d239a1300933","year":2008},"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":68,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.513127Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:00f759891a56dddfc5dd3891493ec0e840c83167fbd7f9dcb1814a9db72349c5","observation_id":"2eb5083a-b75a-419d-a067-94491b81dc9b","resolution":{"observed_at":"2026-08-07T13:12:04.527737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:04.298484Z","title":"Fisher consistency for prior probability shift","venue":null,"work_id":"b9d87ffa-65d6-49b2-9ecc-987f12048e8a","year":2017},"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":69,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.590367Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:e17c79093f790ad07546727d9b450486221010261cef508f1814a7c3a2529a34","observation_id":"a18133ea-ea1d-47d9-8a5d-373385f2c7fc","resolution":{"observed_at":"2026-08-07T13:12:04.359187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:04.099213Z","title":"Elsa: Efficient label shift adaptation through the lens of semiparametric models","venue":null,"work_id":"45447841-edb9-4f21-8759-b0488eb9b5a3","year":2023},"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":70,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.698996Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:2bd49476a8d3cc42af03a6613c1d1ebf7c8c9f46027e08f34af77089b53fd04b","observation_id":"b6a5e29c-d47a-4fe5-a83b-c9706694135e","resolution":{"observed_at":"2026-08-07T13:12:04.173028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.08527","last_updated":"2024-06-04T19:38:02Z","snapshot_observed_at":"2026-07-06T16:47:37.217467Z","submitted_at":"2023-11-14T20:42:52Z","title":"Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments","version":3},"cited_work":{"arxiv_id":"2311.08527","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.08527","snapshot_observed_at":"2026-08-07T13:12:00.413058Z","title":"Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments","venue":"stat.AP","work_id":"aa86b1fd-ae29-4007-87b2-27e924fa7448","year":2023},"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":71,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.783172Z"},"links":{"cited_paper":"/paper/2311.08527","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:d72d1c8cff8af5851aaeafb46f7bd99e6ea5c4fdc068f715008e95c3a876fbbf","observation_id":"a6585666-25e6-4127-b09f-9be0c6a6747a","resolution":{"observed_at":"2026-08-07T13:12:00.561495Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:03.842917Z","title":null,"venue":null,"work_id":"58ba85e0-21a6-4d22-bb72-c89b560cfbd4","year":2006},"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":72,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.886495Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:9619fbc28b44f5a42a1df6a5adc1682cd5af5d069a71baa3e602498d8608cfe0","observation_id":"96a440a4-b54a-4056-b800-51fa2a5695a0","resolution":{"observed_at":"2026-08-07T13:12:03.972088Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:03.639757Z","title":null,"venue":null,"work_id":"39ca60b1-e6e4-4639-ba22-28839cd38be5","year":2011},"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":73,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:58.965965Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:70644ba7459940b9e7fcdab7d4082696fc4739f3b6a36b15e64747db9322ea5d","observation_id":"df33d49d-1ce9-4889-b29f-8a83b14c858e","resolution":{"observed_at":"2026-08-07T13:12:03.721176Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:03.455080Z","title":"J., Polley, E","venue":null,"work_id":"da83c365-257b-4959-b34d-cf146afc6897","year":2007},"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":74,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.061307Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:7a0c71b95c42a1f82f2bdcd913fd59e1a17862560fd90517a981fefce151ba3c","observation_id":"35fcd89b-d472-43b4-af08-c17d160a6f13","resolution":{"observed_at":"2026-08-07T13:12:03.541584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:03.233466Z","title":null,"venue":null,"work_id":"11e5e2d2-f298-46c0-bac1-4a82abae4c62","year":1998},"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":75,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.165820Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:316d98848fcc3f73eaf1f92b680920bff2e13219822d18dab477b82696ba4b1f","observation_id":"43f64d25-2fe7-4fec-9fb5-1b05e713540a","resolution":{"observed_at":"2026-08-07T13:12:03.357832Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"1503.06388","last_updated":"2016-04-30T22:32:52Z","snapshot_observed_at":"2026-08-07T03:01:08.701439Z","submitted_at":"2015-03-22T06:07:14Z","title":"Adaptive Concentration of Regression Trees, with Application to Random Forests","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.06388","snapshot_observed_at":"2026-08-07T13:11:59.280215Z","title":"and Walther, G","venue":null,"work_id":null,"year":2015},"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":76,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.280215Z"},"links":{"cited_paper":"/paper/1503.06388","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:98a0f65d0294d347a58048b876a6053fd76efae412dbea131e9e7fbb7592a3d4","observation_id":"19d2bd4e-cae0-49ae-bb44-2394af21b441","resolution":{"observed_at":"2026-08-07T13:11:59.280215Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:02.953062Z","title":"H., and Leek, J","venue":null,"work_id":"0e015e3b-d27a-4d92-99a3-a2bc15f79c0a","year":2020},"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":77,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.361049Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:7b1dc6d20d2deea176d8653c156f08bd4e937fd7cf0ba2b115a4222b80dcd335","observation_id":"6d67089b-380c-451c-bb71-68d49a130d6a","resolution":{"observed_at":"2026-08-07T13:12:03.058432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:02.774193Z","title":"Usb: A unified semi-supervised learning benchmark for classification","venue":null,"work_id":"8ad6e00a-4b34-480a-b524-dcba4f8f0e02","year":2022},"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":78,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.491578Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:4bc6b0e22739f80a2ccd715825b6aeb728088d9ec58571afd69b896a57582058","observation_id":"b17fd698-2ba3-4236-9e19-6433a0eb8437","resolution":{"observed_at":"2026-08-07T13:12:02.837813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:02.648454Z","title":"and Lafferty, J","venue":null,"work_id":"32390593-b1dd-46bc-9363-4ceb49dac9dd","year":2008},"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":79,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.580104Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:665b848bcd384cf46dcbbe9e4b96fbc9179b7aaad69c981b6a1efa213ffad42a","observation_id":"a2391b80-e3de-4969-ae9f-ed594ba49a1b","resolution":{"observed_at":"2026-08-07T13:12:02.707271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:02.369481Z","title":null,"venue":null,"work_id":"53f8b0dc-be8c-40b2-8495-23187220e662","year":2017},"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":80,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.641949Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:17d5996e1785064a1447c48cbc65a24ef8687b5fe71b2f22bdf56a10111ded6b","observation_id":"50ab7d85-b0aa-42e7-956f-266676135696","resolution":{"observed_at":"2026-08-07T13:12:02.527777Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:02.062233Z","title":null,"venue":null,"work_id":"2e8d6027-1ef2-4241-999b-0cd09cc04e29","year":2021},"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":81,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.777972Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:5db663d39144a179d9d14cccb1bb1439def270dddc60a045a0990095bce220cd","observation_id":"991de84e-38ef-4dca-9d61-a9cfbc9bcff8","resolution":{"observed_at":"2026-08-07T13:12:02.217674Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:11:59.896028Z","title":"D., and Cai, T","venue":null,"work_id":null,"year":2019},"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":82,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.896028Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:ccf8eefa1eec5e1453673d4dfc75521b698aa0fb782a62eb04737406a0d17dac","observation_id":"e40f7f6a-8843-409f-9105-7b382974d7f8","resolution":{"observed_at":"2026-08-07T13:11:59.896028Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:01.880477Z","title":"Domain adaptation under target and conditional shift","venue":null,"work_id":"3e0f5869-a22d-495c-bf28-80f715243eec","year":2013},"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":83,"source":"arxiv_source","source_observed_at":"2026-08-07T13:11:59.985757Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:1fadaf708979814e8c75b26251c7aaa3ca2b3fe9d2c1548881021c4810fa1803","observation_id":"7c8b772f-56ce-46b8-9934-fa7400f23693","resolution":{"observed_at":"2026-08-07T13:12:01.983615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11922","last_updated":"2024-01-30T19:02:37Z","snapshot_observed_at":"2026-07-06T16:50:02.155961Z","submitted_at":"2023-11-20T16:59:18Z","title":"Evaluating the Surrogate Index as a Decision-Making Tool Using 200 A/B Tests at Netflix","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11922","snapshot_observed_at":"2026-08-07T13:12:00.097395Z","title":"Evaluating the surrogate index as a decision-making tool using 200 a/b tests at netflix","venue":null,"work_id":null,"year":2023},"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":84,"source":"arxiv_source","source_observed_at":"2026-08-07T13:12:00.097395Z"},"links":{"cited_paper":"/paper/2311.11922","citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:8d011b1f5aa28482d55eeace755e1785dac1ba34ffb6bf040e8d92f7445acaa0","observation_id":"85aa99f3-c52f-4a26-af7d-5156a6064ea9","resolution":{"observed_at":"2026-08-07T13:12:00.097395Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:01.604407Z","title":"and Bradic, J","venue":null,"work_id":"d64b992e-5032-4ec9-a021-19e43da69833","year":2022},"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":85,"source":"arxiv_source","source_observed_at":"2026-08-07T13:12:00.189229Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:7aedbbdbce5624e538fd4a5abb0107d5a287364f8ee6df2e36b581e2a99c4073","observation_id":"c628a8d2-3a12-4fd4-8993-dbc96aab7f22","resolution":{"observed_at":"2026-08-07T13:12:01.731510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:12:01.386605Z","title":null,"venue":null,"work_id":"29f63d5c-9f83-420a-a369-2a721b30d57e","year":2005},"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":86,"source":"arxiv_source","source_observed_at":"2026-08-07T13:12:00.270202Z"},"links":{"citing_paper":"/paper/2505.22632"},"observation_digest":"sha256:a986669a7756f7441e5c8eebeaf33d3f1a85439e2be0c848d58093eb491295e7","observation_id":"d58ae6fc-be8f-43e1-af93-c92c6e37dfcd","resolution":{"observed_at":"2026-08-07T13:12:01.469392Z","resolver_source":"raw_fallback","status":"unresolved"},"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"}}],"paper":{"arxiv_id":"2505.22632","last_updated":"2025-05-28T17:50:20Z","latest_version":1,"primary_category":"stat.ME","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"},"reference_resolution":{"displayed":86,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":33,"verified_exact":2,"verified_fuzzy":50},"total_outbound_references":86},"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 86 of 86 outbound references and 1 inbound Pith citation observation for arXiv:2505.22632."}