{"as_of":"2026-07-31T20:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fa86853994870811c1e403f0d377bc7c2132cd3e8420190bf1528774339741d9","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-07-31T06:34:12.847434+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-01T06:18:50.063428Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T09:45:39.788879Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2407.14861","last_updated":"2026-05-06T07:17:56Z","snapshot_observed_at":"2026-07-06T18:49:29.706554Z","submitted_at":"2024-07-20T12:42:24Z","title":"Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomes","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-23T22:37:40.560030Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2407.14861"},"observation_digest":"sha256:88245d4dd19d66a125d266d98bfadf14d55159e51fe08c60b0d0cbf143e1575e","observation_id":"e3aefcfc-3aa0-4909-820d-c71a582b3fea","resolution":{"observed_at":"2026-05-23T22:38:32.650800Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2502.11008","last_updated":"2026-04-11T04:52:11Z","snapshot_observed_at":"2026-07-06T20:37:13.753772Z","submitted_at":"2025-02-16T06:19:37Z","title":"CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-23T03:09:38.961123Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2502.11008"},"observation_digest":"sha256:f4b30c5683a576d7d199ffc2f2684d3d29efb11f5a56a43ac78e8f41a1ccdfb5","observation_id":"3fdd9e1c-624c-47da-bd7e-047edcfb53e0","resolution":{"observed_at":"2026-05-23T03:12:28.509131Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2604.24116","last_updated":"2026-04-27T07:11:57Z","snapshot_observed_at":"2026-07-06T23:10:12.016186Z","submitted_at":"2026-04-27T07:11:57Z","title":"Closing the Loop: A Software Framework for AI to Support Business Decision Making","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-08T03:25:07.868945Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2604.24116"},"observation_digest":"sha256:15441441ede0a58d2d3f51d61945cffb9d906e4b5f446601ae31588c5b5a04c7","observation_id":"ef956bc0-232d-4db8-970c-14d0adb4777f","resolution":{"observed_at":"2026-05-11T22:06:24.365021Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2604.25061","last_updated":"2026-04-27T23:23:12Z","snapshot_observed_at":"2026-07-06T23:10:57.164248Z","submitted_at":"2026-04-27T23:23:12Z","title":"Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T17:57:57.842925Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2604.25061"},"observation_digest":"sha256:152f7d3ec0aef6231483efc300cee5be2d627e107e1d7ca4e7039236bb62e55b","observation_id":"4da0c2ef-a35e-444f-9daf-04e39c7f52ff","resolution":{"observed_at":"2026-05-11T23:11:18.431065Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2604.27741","last_updated":"2026-04-30T11:31:42Z","snapshot_observed_at":"2026-07-06T23:13:11.623453Z","submitted_at":"2026-04-30T11:31:42Z","title":"Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-07T05:43:28.878233Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2604.27741"},"observation_digest":"sha256:adac5e49b3035d2698a3b866d4df3e7823da1545369ae5bfe45d31c64489a653","observation_id":"b7f8793d-8ebe-4aed-b7bc-f68e06428b9b","resolution":{"observed_at":"2026-05-12T10:31:30.464262Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2605.10430","last_updated":"2026-05-23T09:40:21Z","snapshot_observed_at":"2026-07-06T23:22:23.287792Z","submitted_at":"2026-05-11T12:04:02Z","title":"Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-05-12T04:38:31.839273Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2605.10430"},"observation_digest":"sha256:7eb241bd822b5041a0dba71aa3f9998eacb71f600278a921c61144ea9c3b56ed","observation_id":"5d101d6d-b2cb-4c93-a4f9-7b2d695bb436","resolution":{"observed_at":"2026-05-12T06:01:26.213323Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2605.10533","last_updated":"2026-05-11T13:19:33Z","snapshot_observed_at":"2026-07-06T23:22:28.318343Z","submitted_at":"2026-05-11T13:19:33Z","title":"ConfoundingSHAP: Quantifying confounding strength in causal inference","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-12T03:23:53.494722Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2605.10533"},"observation_digest":"sha256:2eafeb677aca48a4ef0ef1bfe985fe429cc9b25c51b0e4a0e90bdc9293419df0","observation_id":"e7d77dba-5d6c-484f-b0e6-63b24cc7a104","resolution":{"observed_at":"2026-05-12T03:26:19.721959Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning","version":2},"cited_work":{"arxiv_id":"2002.11631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.11631","snapshot_observed_at":"2026-07-01T09:45:39.788879Z","title":"arXiv preprint arXiv:2002.11631 (2020)","venue":null,"work_id":"898cb3cc-4749-40eb-921f-7d5d33753d97","year":2020},"citing_paper":{"arxiv_id":"2606.30932","last_updated":"2026-06-29T21:34:37Z","snapshot_observed_at":"2026-07-07T00:04:44.146481Z","submitted_at":"2026-06-29T21:34:37Z","title":"Personalizing Marketplace Policies with Competing Objectives and Constrained Experiments: Evidence from a Job Marketplace","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-01T06:18:50.063428Z"},"links":{"cited_paper":"/paper/2002.11631","citing_paper":"/paper/2606.30932"},"observation_digest":"sha256:2048bf58f7fca992a6023406c36d70c58b7e5348969f5eada9b1d8ce8c06da75","observation_id":"00241179-e75b-4f85-abf0-0ed9e83de9ad","resolution":{"observed_at":"2026-07-01T09:45:39.790211Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2002.11631/citation-record","integrity":"/paper/2002.11631/integrity","json":"/paper/2002.11631/citation-record.json","paper":"/paper/2002.11631"},"outbound":[],"paper":{"arxiv_id":"2002.11631","last_updated":"2020-03-02T18:34:29Z","latest_version":2,"primary_category":"cs.CY","snapshot_observed_at":"2026-07-06T09:00:20.882042Z","submitted_at":"2020-02-25T17:35:33Z","title":"CausalML: Python Package for Causal Machine Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-07-31T06:34:12.847434+00:00","source":"crossref"},{"observed_at":"2026-07-31T06:34:08.642788+00:00","source":"retraction_watch"}],"thesis":"As of 31 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2002.11631."}