{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FE6FEC3MVPTHC3NDXB73N22XSK","short_pith_number":"pith:FE6FEC3M","canonical_record":{"source":{"id":"2506.13107","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T05:32:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"279dc25f96eeef86de7fd657bc58dc923f1f579562d34ca4e59ad4738bd73ca5","abstract_canon_sha256":"3a5b7c610eac6e5f2c73e94651fba8f64160d43ed2a88113246eeb2693330b32"},"schema_version":"1.0"},"canonical_sha256":"293c520b6cabe6716da3b87fb6eb5792a7f21cf7342857bec4f5f48b1a18a04a","source":{"kind":"arxiv","id":"2506.13107","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13107","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13107v4","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13107","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"FE6FEC3MVPTH","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"FE6FEC3MVPTHC3ND","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"FE6FEC3M","created_at":"2026-06-03T01:05:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FE6FEC3MVPTHC3NDXB73N22XSK","target":"record","payload":{"canonical_record":{"source":{"id":"2506.13107","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T05:32:58Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"279dc25f96eeef86de7fd657bc58dc923f1f579562d34ca4e59ad4738bd73ca5","abstract_canon_sha256":"3a5b7c610eac6e5f2c73e94651fba8f64160d43ed2a88113246eeb2693330b32"},"schema_version":"1.0"},"canonical_sha256":"293c520b6cabe6716da3b87fb6eb5792a7f21cf7342857bec4f5f48b1a18a04a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-03T01:05:44.252056Z","signature_b64":"k7E1r9Pol2J7B0BAzxvz++wEcCG3RD4sc8kGlkDQUH/ai+vs5/owx5QocT5umQ2ZFDAy0ixIK6EYZ81zjWErCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"293c520b6cabe6716da3b87fb6eb5792a7f21cf7342857bec4f5f48b1a18a04a","last_reissued_at":"2026-06-03T01:05:44.251597Z","signature_status":"signed_v1","first_computed_at":"2026-06-03T01:05:44.251597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.13107","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-03T01:05:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9WBuw4VuKoD06xFuXuiyvkGGVQB2OSPrzXcBQvMUx+VBYcXxraLUZ4NK3YtuxwBMYu0wf8lrMiGcHFFBwZwXDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T23:37:27.707989Z"},"content_sha256":"7aacb3f1b441ea52a8df2a564897e8f8c9306692028e3e195583a947baf66a68","schema_version":"1.0","event_id":"sha256:7aacb3f1b441ea52a8df2a564897e8f8c9306692028e3e195583a947baf66a68"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FE6FEC3MVPTHC3NDXB73N22XSK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Honesty in Causal Forests: When It Helps and When It Hurts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Carlos Fern\\'andez-Lor\\'ia, Yanfang Hou","submitted_at":"2025-06-16T05:32:58Z","abstract_excerpt":"Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard practice is honest estimation: dividing the data into two samples, one to define subgroups and another to estimate treatment effects within them. This is intended to reduce overfitting and is the default in many software packages. But is it the right choice? We show that honest estimation can reduce the accuracy of estimates of individual treatment effects, especially when effect heterogeneity is substantial and datasets ar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13107","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.13107/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-03T01:05:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NhtnFsQ6vy/K+XuCyjPMLtjU56rYFIshEJ3Ft2mzNZLtxBoI0cVzg0IfzR/PUNm0aAomDeWdqvvsT5jy3XoIDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-13T23:37:27.708371Z"},"content_sha256":"122ef3101a5a9f6fa3f63d06f97679a1d5af2ac9d3e2eef35fa978c26c2771e7","schema_version":"1.0","event_id":"sha256:122ef3101a5a9f6fa3f63d06f97679a1d5af2ac9d3e2eef35fa978c26c2771e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FE6FEC3MVPTHC3NDXB73N22XSK/bundle.json","state_url":"https://pith.science/pith/FE6FEC3MVPTHC3NDXB73N22XSK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FE6FEC3MVPTHC3NDXB73N22XSK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-13T23:37:27Z","links":{"resolver":"https://pith.science/pith/FE6FEC3MVPTHC3NDXB73N22XSK","bundle":"https://pith.science/pith/FE6FEC3MVPTHC3NDXB73N22XSK/bundle.json","state":"https://pith.science/pith/FE6FEC3MVPTHC3NDXB73N22XSK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FE6FEC3MVPTHC3NDXB73N22XSK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FE6FEC3MVPTHC3NDXB73N22XSK","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":"3a5b7c610eac6e5f2c73e94651fba8f64160d43ed2a88113246eeb2693330b32","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T05:32:58Z","title_canon_sha256":"279dc25f96eeef86de7fd657bc58dc923f1f579562d34ca4e59ad4738bd73ca5"},"schema_version":"1.0","source":{"id":"2506.13107","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13107","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13107v4","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13107","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_12","alias_value":"FE6FEC3MVPTH","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_16","alias_value":"FE6FEC3MVPTHC3ND","created_at":"2026-06-03T01:05:44Z"},{"alias_kind":"pith_short_8","alias_value":"FE6FEC3M","created_at":"2026-06-03T01:05:44Z"}],"graph_snapshots":[{"event_id":"sha256:122ef3101a5a9f6fa3f63d06f97679a1d5af2ac9d3e2eef35fa978c26c2771e7","target":"graph","created_at":"2026-06-03T01:05:44Z","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/2506.13107/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Causal forests estimate how treatment effects vary across individuals, guiding personalized interventions in areas like marketing, operations, and public policy. A standard practice is honest estimation: dividing the data into two samples, one to define subgroups and another to estimate treatment effects within them. This is intended to reduce overfitting and is the default in many software packages. But is it the right choice? We show that honest estimation can reduce the accuracy of estimates of individual treatment effects, especially when effect heterogeneity is substantial and datasets ar","authors_text":"Carlos Fern\\'andez-Lor\\'ia, Yanfang Hou","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T05:32:58Z","title":"Honesty in Causal Forests: When It Helps and When It Hurts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13107","kind":"arxiv","version":4},"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":"sha256:7aacb3f1b441ea52a8df2a564897e8f8c9306692028e3e195583a947baf66a68","target":"record","created_at":"2026-06-03T01:05:44Z","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":"3a5b7c610eac6e5f2c73e94651fba8f64160d43ed2a88113246eeb2693330b32","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-16T05:32:58Z","title_canon_sha256":"279dc25f96eeef86de7fd657bc58dc923f1f579562d34ca4e59ad4738bd73ca5"},"schema_version":"1.0","source":{"id":"2506.13107","kind":"arxiv","version":4}},"canonical_sha256":"293c520b6cabe6716da3b87fb6eb5792a7f21cf7342857bec4f5f48b1a18a04a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"293c520b6cabe6716da3b87fb6eb5792a7f21cf7342857bec4f5f48b1a18a04a","first_computed_at":"2026-06-03T01:05:44.251597Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-03T01:05:44.251597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"k7E1r9Pol2J7B0BAzxvz++wEcCG3RD4sc8kGlkDQUH/ai+vs5/owx5QocT5umQ2ZFDAy0ixIK6EYZ81zjWErCA==","signature_status":"signed_v1","signed_at":"2026-06-03T01:05:44.252056Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.13107","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7aacb3f1b441ea52a8df2a564897e8f8c9306692028e3e195583a947baf66a68","sha256:122ef3101a5a9f6fa3f63d06f97679a1d5af2ac9d3e2eef35fa978c26c2771e7"],"state_sha256":"89af568e8fded27e831dbcdd63385e403da74825e34bc1440cff426277e4a570"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nsDgtG6vjL0SVWFecg5duDoSt/EY3BpD8f9V0Nm5cJuN66kKZzYr3h8FhP5aXeuFFv+AhIyb9MWcjCfOGN2wCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-13T23:37:27.710762Z","bundle_sha256":"ceac33439a0a63c97e56f4f08db04daa1817ab07b05d50b50f8082c1479c12d9"}}