{"as_of":"2026-08-07T06:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:27c90df31a5e74e0e101d60e690b5b813305423d339bc65551210b1009a0ed91","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T08:00:21.964524Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.08122/citation-record","integrity":"/paper/2607.08122/integrity","json":"/paper/2607.08122/citation-record.json","paper":"/paper/2607.08122"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T08:00:19.695648Z","title":"Liang, and Chao Yan","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:19.695648Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:11e7486e08bc640f01756d18ad7509fcae6fb629bf68bfe46fc0f4b870c56b4b","observation_id":"7df78db9-bfaf-4b51-85c9-bf77cc9e0442","resolution":{"observed_at":"2026-08-02T08:00:19.695648Z","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":"2026-08-02T08:00:21.512638Z","title":null,"venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:21.512638Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:c12aa5c921e87721a33206c26c1087640a2e43e5b0850f5001c17a8280b3397f","observation_id":"6e6341a5-ffda-44cb-b204-62e4667a8a2b","resolution":{"observed_at":"2026-08-02T08:00:21.512638Z","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":"2026-08-02T08:00:21.353199Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:21.353199Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:efc89dd8c147fc813313181fb4f1315d36f80f7890d9a1da9f79c4e4ecf30845","observation_id":"18f045eb-8e58-4d69-b6af-25b0a5b85acc","resolution":{"observed_at":"2026-08-02T08:00:21.353199Z","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":"2026-08-02T08:00:20.126580Z","title":"Christos Louizos, Uri Shalit, Joris M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.126580Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:ab67bb260a5030a90380f3040957cd1def2b6ead650ad6514c69814bcbbfd9dc","observation_id":"76baef2c-961f-4e7f-a96a-952b3fb1cd5d","resolution":{"observed_at":"2026-08-02T08:00:20.126580Z","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":"2026-08-02T08:00:20.474505Z","title":"Fengshi Niu, Harsha Nori, Brian Quistorff, Rich Caruana, Donald Ngwe, and Aadharsh Kannan","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.474505Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:c33da6c2793973f5f1195359092da43330879a57ec770afe199e27d178e5ecec","observation_id":"56dd2e3a-20f4-4fb6-8f76-3d97b88f50c8","resolution":{"observed_at":"2026-08-02T08:00:20.474505Z","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":"2026-08-02T08:00:20.355355Z","title":"Jeffrey W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.355355Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:a48a94e8fdc9e800671c939d5463263eb273695de9e9be99933026526b95c86e","observation_id":"c6cdf627-b2c8-4451-8324-24d4936af956","resolution":{"observed_at":"2026-08-02T08:00:20.355355Z","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":"2026-08-02T08:00:21.185120Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:21.185120Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:f57566af94f45cb68c638a48b60688050652fbeae72eb192cb7da028cd4d7662","observation_id":"917c46d2-ea24-47a6-9a83-30530789ebd7","resolution":{"observed_at":"2026-08-02T08:00:21.185120Z","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":"2026-08-02T08:00:21.665887Z","title":"The bias-aware variant of Appendix F replaces TM by TM + \\Approx(ϕ)2","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:21.665887Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:6f1aaca9ba2e2f85b9796c8dc64d7b21f455bf825294c5098e6b66773ac2ef5b","observation_id":"7055e8f6-b9b7-4144-9919-e7169c222198","resolution":{"observed_at":"2026-08-02T08:00:21.665887Z","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":"2026-08-02T08:00:21.843704Z","title":"•Synthetic sample size: varyn syn/n∈ {1,2,5,10,50}","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:21.843704Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:13401f64a6d75e1fbc5e889c7dc0965b864b8fab97ceafdec8082d003988ed68","observation_id":"26164815-3a4b-4b4c-bb33-77bd80d6ba37","resolution":{"observed_at":"2026-08-02T08:00:21.843704Z","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":"2026-08-02T08:00:21.964524Z","title":"Rows index sample size n∈ {1000,5000,20000} and columns index ε∈ {0.5,1,2,5}","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:21.964524Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:2dc596f9853bcb131907c0ea9c6647d48392d76b50136920b0e6416493a0aa5e","observation_id":"1c8e784c-7829-4f30-bf98-e071722d6206","resolution":{"observed_at":"2026-08-02T08:00:21.964524Z","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":"2026-08-02T08:00:20.078927Z","title":"Model agnostic differentially private causal inference.arXiv preprint arXiv:2505.19589,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":1986,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.078927Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:f3a437acbd73f3da1866b348d946bc39fb2dc5cf4c6f05ba85b8ea9c8c41bafb","observation_id":"5237cb67-3a01-4ded-a294-1f595c35b4c4","resolution":{"observed_at":"2026-08-02T08:00:20.078927Z","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":"2026-08-02T08:00:20.936856Z","title":"PrivATE: Differentially private confidence in- tervals for average treatment effects.arXiv preprint arXiv:2505.21641, 2025a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":1987,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.936856Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:d59a0350a190b87f2eb4573c00683e51ecd552d8915e26a13bfe7c97f5b611d6","observation_id":"00a30c39-e216-4de6-9d89-aabff20015f2","resolution":{"observed_at":"2026-08-02T08:00:20.936856Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.09238","last_updated":"2022-02-15T16:08:22Z","snapshot_observed_at":"2026-07-06T12:19:47.730180Z","submitted_at":"2021-12-16T22:49:53Z","title":"Benchmarking Differentially Private Synthetic Data Generation Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.09238","snapshot_observed_at":"2026-08-02T08:00:21.063317Z","title":"Benchmarking differentially private synthetic data generation algorithms","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:21.063317Z"},"links":{"cited_paper":"/paper/2112.09238","citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:3f9ae1ed25c91d50393cbb603612fbbba9a6dcfeffcf70660ba3ed02b01bfadc","observation_id":"8778459c-1acc-402a-92b1-228c9408d177","resolution":{"observed_at":"2026-08-02T08:00:21.063317Z","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":"2026-08-02T08:00:19.762298Z","title":"Claire McKay Bowen and Fang Liu","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:19.762298Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:e8f0645a0c4065dabeeaf61e997deb88257b0a940886c54b888463a193ac21aa","observation_id":"b0877fae-8734-4918-a4be-4be77ee3dee9","resolution":{"observed_at":"2026-08-02T08:00:19.762298Z","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":"2026-08-02T08:00:19.884609Z","title":"Moritz Hardt, Katrina Ligett, and Frank McSherry","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:19.884609Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:45b18d37691338e7bae80546561446074ca03b7c7cc213525f51b44c320103c2","observation_id":"5a3314c2-9991-47bf-a119-ecda11af0358","resolution":{"observed_at":"2026-08-02T08:00:19.884609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.04978","last_updated":"2021-08-11T00:49:48Z","snapshot_observed_at":"2026-08-06T23:27:16.511282Z","submitted_at":"2021-08-11T00:49:48Z","title":"Winning the NIST Contest: A scalable and general approach to differentially private synthetic data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.04978","snapshot_observed_at":"2026-08-02T08:00:20.235477Z","title":"Win- ning the NIST contest: A scalable and general approach to differentially private synthetic data.arXiv preprint arXiv:2108.04978,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.235477Z"},"links":{"cited_paper":"/paper/2108.04978","citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:2c0e218148814b42ea0bf99b1a830a0e4eae7e6171a088b5f3462dce226e0c2c","observation_id":"0b57a3c2-db98-45f9-9317-ff825b0db5b1","resolution":{"observed_at":"2026-08-02T08:00:20.235477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14789","last_updated":"2025-08-16T04:30:38Z","snapshot_observed_at":"2026-08-05T19:07:41.596688Z","submitted_at":"2024-10-18T18:02:13Z","title":"Differentially Private Covariate Balancing Causal Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14789","snapshot_observed_at":"2026-08-02T08:00:20.612276Z","title":"Differentially private covariate balancing causal inference.arXiv preprint arXiv:2410.14789,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.612276Z"},"links":{"cited_paper":"/paper/2410.14789","citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:cd1db785ab73e1d06f2649ce3e66aafe3f48218cb1c78b775e219b9050f4e1ec","observation_id":"f5e92107-d8e3-486b-9d0e-9344c59eb341","resolution":{"observed_at":"2026-08-02T08:00:20.612276Z","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":"2026-08-02T08:00:20.798782Z","title":"Trivellore E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:20.798782Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:628f41487b0a9b9f79be12ac79923797b8f605195e4dd1a1f9da80467badb651","observation_id":"e18c71b6-5ad6-4934-ad5f-0285d6144480","resolution":{"observed_at":"2026-08-02T08:00:20.798782Z","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":"2026-08-02T08:00:19.970287Z","title":"Preprint; PMCID: PMC13015641","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration","version":2},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-08-02T08:00:19.970287Z"},"links":{"citing_paper":"/paper/2607.08122"},"observation_digest":"sha256:b92a0d74842af91e4aa1036d29f08fcde7e799bd2c15a8d675aae4e6a8f5626d","observation_id":"270943e4-a011-4736-bec0-8bc55e6d3e4b","resolution":{"observed_at":"2026-08-02T08:00:19.970287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.08122","last_updated":"2026-07-17T04:28:43Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T01:12:11.821594Z","submitted_at":"2026-07-09T05:44:03Z","title":"Workload-Preserving Differentially Private Synthetic Data for Causal Inference via Maximum-Entropy Calibration"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":19},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2607.08122."}