{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:WW7A5J5PYDBIHPZKPP2JSPEQWE","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":"4537892fcc27cdcae921998be39d1d1e4bd7b711ab434128f1e0ad3dfa5479c4","cross_cats_sorted":["cs.CY","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-31T05:38:36Z","title_canon_sha256":"6cd0c53087edba40ea5ef045bc405adf73465c34e8d6093b757b5686b6e91b55"},"schema_version":"1.0","source":{"id":"2108.13628","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.13628","created_at":"2026-07-05T06:33:39Z"},{"alias_kind":"arxiv_version","alias_value":"2108.13628v2","created_at":"2026-07-05T06:33:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.13628","created_at":"2026-07-05T06:33:39Z"},{"alias_kind":"pith_short_12","alias_value":"WW7A5J5PYDBI","created_at":"2026-07-05T06:33:39Z"},{"alias_kind":"pith_short_16","alias_value":"WW7A5J5PYDBIHPZK","created_at":"2026-07-05T06:33:39Z"},{"alias_kind":"pith_short_8","alias_value":"WW7A5J5P","created_at":"2026-07-05T06:33:39Z"}],"graph_snapshots":[{"event_id":"sha256:126e8d55855ab85792e3886f188796449a57bc1f3e45899a0c4b6fa6cabb2d47","target":"graph","created_at":"2026-07-05T06:33:39Z","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/2108.13628/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider the problem of learning an optimal prescriptive tree (i.e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observational data. This problem arises in numerous socially important domains such as public health and personalized medicine, where interpretable and data-driven interventions are sought based on data gathered in deployment -- through passive collection of data -- rather than from randomized trials. We propose a method for learning optimal prescriptive trees using mixed-integer optimization (MIO) technology. We show that un","authors_text":"Andr\\'es G\\'omez, Nathanael Jo, Phebe Vayanos, Sina Aghaei","cross_cats":["cs.CY","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-31T05:38:36Z","title":"Learning Optimal Prescriptive Trees from Observational Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.13628","kind":"arxiv","version":2},"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":"sha256:261016992edb3e006c55b9665d662d9f64a1a0062a9fc0282b6bdc83d2905f67","target":"record","created_at":"2026-07-05T06:33:39Z","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":"4537892fcc27cdcae921998be39d1d1e4bd7b711ab434128f1e0ad3dfa5479c4","cross_cats_sorted":["cs.CY","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-31T05:38:36Z","title_canon_sha256":"6cd0c53087edba40ea5ef045bc405adf73465c34e8d6093b757b5686b6e91b55"},"schema_version":"1.0","source":{"id":"2108.13628","kind":"arxiv","version":2}},"canonical_sha256":"b5be0ea7afc0c283bf2a7bf4993c90b13fc38f1e57c78d64fd651a295dcf040b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5be0ea7afc0c283bf2a7bf4993c90b13fc38f1e57c78d64fd651a295dcf040b","first_computed_at":"2026-07-05T06:33:39.151298Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:33:39.151298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GRW4MSqUm59R026Qzhr8tJrozfAbf+v3iGFS0QLZE6j9g+TjKGhIMF1o311/zcULbyCVBtEDjmfbSJfHl+wTBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:33:39.151848Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.13628","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:261016992edb3e006c55b9665d662d9f64a1a0062a9fc0282b6bdc83d2905f67","sha256:126e8d55855ab85792e3886f188796449a57bc1f3e45899a0c4b6fa6cabb2d47"],"state_sha256":"742f1028319c9d5e978be91e736415f94d8fc3a274ef664c39713abb69e215f0"}