{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:64B7IWS5W7Y7ZRAZWXCPER6ZME","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"823eb782a295aac4de35992017b54525a54706c1d0687de264f59cd50d32e5f1","cross_cats_sorted":["econ.GN","q-fin.EC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-04-23T01:36:24Z","title_canon_sha256":"6245edd11e9b351069abf5ee83fbfc75afc1ad0761c59246613483d6168c769f"},"schema_version":"1.0","source":{"id":"2204.10971","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.10971","created_at":"2026-07-05T04:17:05Z"},{"alias_kind":"arxiv_version","alias_value":"2204.10971v1","created_at":"2026-07-05T04:17:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.10971","created_at":"2026-07-05T04:17:05Z"},{"alias_kind":"pith_short_12","alias_value":"64B7IWS5W7Y7","created_at":"2026-07-05T04:17:05Z"},{"alias_kind":"pith_short_16","alias_value":"64B7IWS5W7Y7ZRAZ","created_at":"2026-07-05T04:17:05Z"},{"alias_kind":"pith_short_8","alias_value":"64B7IWS5","created_at":"2026-07-05T04:17:05Z"}],"graph_snapshots":[{"event_id":"sha256:9cf4f5134947095a6531d95e3cd2895b7cec1d0fa6d0f79cfac6ef9d5ef339f1","target":"graph","created_at":"2026-07-05T04:17:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2204.10971/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness (CE) analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules (ITRs) that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective ITR (CE-ITR) under the causal inference framework that allows proper handling of potential confounding and can be applied to","authors_text":"Adam P. Bress, Andrew S. Moran, Brandon K. Bellows, Jincheng Shen, Paul Kolm, Tom H. Greene, William S.Weintraub, Yizhe Xu, Yue Zhang, Zugui Zhang","cross_cats":["econ.GN","q-fin.EC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-04-23T01:36:24Z","title":"An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.10971","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d2effd093a866cb3699629651ed003b059e83ab88c2f176b0b265f36f8a05e15","target":"record","created_at":"2026-07-05T04:17:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"823eb782a295aac4de35992017b54525a54706c1d0687de264f59cd50d32e5f1","cross_cats_sorted":["econ.GN","q-fin.EC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2022-04-23T01:36:24Z","title_canon_sha256":"6245edd11e9b351069abf5ee83fbfc75afc1ad0761c59246613483d6168c769f"},"schema_version":"1.0","source":{"id":"2204.10971","kind":"arxiv","version":1}},"canonical_sha256":"f703f45a5db7f1fcc419b5c4f247d9610f3fad7b286da6f04b3272bdf6a64852","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f703f45a5db7f1fcc419b5c4f247d9610f3fad7b286da6f04b3272bdf6a64852","first_computed_at":"2026-07-05T04:17:05.736697Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:17:05.736697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"weLUJc2GGAypaMsXRV29py3H3nQhw69HtVaPRwKcljMmGLmczwjio58MlSZ83RrHxzC71AdIbCQw+dZmwrMeDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:17:05.737106Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.10971","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d2effd093a866cb3699629651ed003b059e83ab88c2f176b0b265f36f8a05e15","sha256:9cf4f5134947095a6531d95e3cd2895b7cec1d0fa6d0f79cfac6ef9d5ef339f1"],"state_sha256":"7352e484474eaa544d513aea0e5fe0897f973fd2bc19a4460909cda60d662e3a"}