{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZPRBGETNF5MFZ6FN2ZZFVL6GI6","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":"cc9cb6e52faa21aa48a7b965b8f6d9bd2e8108a57fcfd96be35f0bb269e1ecdc","cross_cats_sorted":["cs.LG","econ.EM","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-02-09T17:03:01Z","title_canon_sha256":"e708d7d557a1922faf7023696eab392be8e7b8c62d9d5cea9bab128459549e70"},"schema_version":"1.0","source":{"id":"1702.02896","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02896","created_at":"2026-07-05T01:33:05Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02896v6","created_at":"2026-07-05T01:33:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02896","created_at":"2026-07-05T01:33:05Z"},{"alias_kind":"pith_short_12","alias_value":"ZPRBGETNF5MF","created_at":"2026-07-05T01:33:05Z"},{"alias_kind":"pith_short_16","alias_value":"ZPRBGETNF5MFZ6FN","created_at":"2026-07-05T01:33:05Z"},{"alias_kind":"pith_short_8","alias_value":"ZPRBGETN","created_at":"2026-07-05T01:33:05Z"}],"graph_snapshots":[{"event_id":"sha256:d6216d5f5bc8bc36cd64ea4ea76e76919a945820d41b07f4d357d522cec4b424","target":"graph","created_at":"2026-07-05T01:33:05Z","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/1702.02896/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application-specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can l","authors_text":"Stefan Wager, Susan Athey","cross_cats":["cs.LG","econ.EM","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-02-09T17:03:01Z","title":"Policy Learning with Observational Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02896","kind":"arxiv","version":6},"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":"sha256:5ad0a46458136b10360c838c3ced7ed6b6b703daadbc3a59ecdbd8d9d5f963c8","target":"record","created_at":"2026-07-05T01:33:05Z","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":"cc9cb6e52faa21aa48a7b965b8f6d9bd2e8108a57fcfd96be35f0bb269e1ecdc","cross_cats_sorted":["cs.LG","econ.EM","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-02-09T17:03:01Z","title_canon_sha256":"e708d7d557a1922faf7023696eab392be8e7b8c62d9d5cea9bab128459549e70"},"schema_version":"1.0","source":{"id":"1702.02896","kind":"arxiv","version":6}},"canonical_sha256":"cbe213126d2f585cf8add6725aafc647ae44a37cf110e63440b73c94b083af58","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cbe213126d2f585cf8add6725aafc647ae44a37cf110e63440b73c94b083af58","first_computed_at":"2026-07-05T01:33:05.307239Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:33:05.307239Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YWX6r53O/0Yc4mmb0UEafB3fhkuAuKkzI+rPN0yk78HwIkOHOB89vwodvNg4PugQ4suc5gQKrMFrBfG5gcJ9Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:33:05.307659Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.02896","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5ad0a46458136b10360c838c3ced7ed6b6b703daadbc3a59ecdbd8d9d5f963c8","sha256:d6216d5f5bc8bc36cd64ea4ea76e76919a945820d41b07f4d357d522cec4b424"],"state_sha256":"d493e3feee66a37eac05cf65ab5745e4f52e1b0381e4f24b2ecfc553ac8018b9"}