{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:C36ICTCHOLHBSU3ZXQRRWQPAO4","short_pith_number":"pith:C36ICTCH","canonical_record":{"source":{"id":"2202.05245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2022-02-10T18:51:52Z","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"47429cfa4e28b987ed207c648eb5d56930b21f745440dd3ce16a596088e37b40","abstract_canon_sha256":"d14a62509152a8bc38d7463899aa17c8dbdfbf2dfbd221184fa716c7433ce632"},"schema_version":"1.0"},"canonical_sha256":"16fc814c4772ce195379bc231b41e0773307265654adad6add70bd838935ab77","source":{"kind":"arxiv","id":"2202.05245","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.05245","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"arxiv_version","alias_value":"2202.05245v2","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.05245","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"pith_short_12","alias_value":"C36ICTCHOLHB","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"pith_short_16","alias_value":"C36ICTCHOLHBSU3Z","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"pith_short_8","alias_value":"C36ICTCH","created_at":"2026-07-05T03:57:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:C36ICTCHOLHBSU3ZXQRRWQPAO4","target":"record","payload":{"canonical_record":{"source":{"id":"2202.05245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2022-02-10T18:51:52Z","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"47429cfa4e28b987ed207c648eb5d56930b21f745440dd3ce16a596088e37b40","abstract_canon_sha256":"d14a62509152a8bc38d7463899aa17c8dbdfbf2dfbd221184fa716c7433ce632"},"schema_version":"1.0"},"canonical_sha256":"16fc814c4772ce195379bc231b41e0773307265654adad6add70bd838935ab77","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:57:31.982828Z","signature_b64":"jgKQ84fys5bXQVZiLZwmRh91A/AkNRWgL5KmCb/2lYSAe+/Bdsw+2sRvrHlcoWf7/WdHm4b5SgalQe+mUU+3BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"16fc814c4772ce195379bc231b41e0773307265654adad6add70bd838935ab77","last_reissued_at":"2026-07-05T03:57:31.982365Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:57:31.982365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.05245","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:57:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EjB0XHUgfif4/EzCQ6MMiPc56KxZxgGu+ZFRvyAQwKdoF0guAEg7P4+ai64MdsgjdsZ8oFiTJ6C91m6vhsWMAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:34:11.040497Z"},"content_sha256":"865577241f2cbde3b864ef73b861a74b25f53a791c228f9d24fd275f59eb4206","schema_version":"1.0","event_id":"sha256:865577241f2cbde3b864ef73b861a74b25f53a791c228f9d24fd275f59eb4206"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:C36ICTCHOLHBSU3ZXQRRWQPAO4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"econ.EM","authors_text":"Masaaki Imaizumi, Masahiro Kato","submitted_at":"2022-02-10T18:51:52Z","abstract_excerpt":"We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning for causal inference, a wide range of large-scale models for causality are gaining attention. One problem is that suspicions have been raised that the large-scale models are prone to overfitting to observations with sample selection, hence the large models may not be suitable for causal prediction. In this study, to resolve the suspicious, we investigate on the validity of causal inference methods for overparameterize"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.05245","kind":"arxiv","version":2},"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/2202.05245/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":"2026-07-05T03:57:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6v2NCNMmHivugfA8XtGRG01k+uKIrfIlXM5h/cMf0Caeu6pd83LOWrCAYxnCVYYNHLgkLakv4h7S9JQiTcntAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:34:11.040889Z"},"content_sha256":"cfee299088af9c47bce72953ba77de415478608c5ff95e07b3def8cc2ade5839","schema_version":"1.0","event_id":"sha256:cfee299088af9c47bce72953ba77de415478608c5ff95e07b3def8cc2ade5839"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C36ICTCHOLHBSU3ZXQRRWQPAO4/bundle.json","state_url":"https://pith.science/pith/C36ICTCHOLHBSU3ZXQRRWQPAO4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C36ICTCHOLHBSU3ZXQRRWQPAO4/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":"2026-07-07T11:34:11Z","links":{"resolver":"https://pith.science/pith/C36ICTCHOLHBSU3ZXQRRWQPAO4","bundle":"https://pith.science/pith/C36ICTCHOLHBSU3ZXQRRWQPAO4/bundle.json","state":"https://pith.science/pith/C36ICTCHOLHBSU3ZXQRRWQPAO4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C36ICTCHOLHBSU3ZXQRRWQPAO4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:C36ICTCHOLHBSU3ZXQRRWQPAO4","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":"d14a62509152a8bc38d7463899aa17c8dbdfbf2dfbd221184fa716c7433ce632","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2022-02-10T18:51:52Z","title_canon_sha256":"47429cfa4e28b987ed207c648eb5d56930b21f745440dd3ce16a596088e37b40"},"schema_version":"1.0","source":{"id":"2202.05245","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.05245","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"arxiv_version","alias_value":"2202.05245v2","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.05245","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"pith_short_12","alias_value":"C36ICTCHOLHB","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"pith_short_16","alias_value":"C36ICTCHOLHBSU3Z","created_at":"2026-07-05T03:57:31Z"},{"alias_kind":"pith_short_8","alias_value":"C36ICTCH","created_at":"2026-07-05T03:57:31Z"}],"graph_snapshots":[{"event_id":"sha256:cfee299088af9c47bce72953ba77de415478608c5ff95e07b3def8cc2ade5839","target":"graph","created_at":"2026-07-05T03:57:31Z","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/2202.05245/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning for causal inference, a wide range of large-scale models for causality are gaining attention. One problem is that suspicions have been raised that the large-scale models are prone to overfitting to observations with sample selection, hence the large models may not be suitable for causal prediction. In this study, to resolve the suspicious, we investigate on the validity of causal inference methods for overparameterize","authors_text":"Masaaki Imaizumi, Masahiro Kato","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2022-02-10T18:51:52Z","title":"Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.05245","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":"sha256:865577241f2cbde3b864ef73b861a74b25f53a791c228f9d24fd275f59eb4206","target":"record","created_at":"2026-07-05T03:57:31Z","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":"d14a62509152a8bc38d7463899aa17c8dbdfbf2dfbd221184fa716c7433ce632","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2022-02-10T18:51:52Z","title_canon_sha256":"47429cfa4e28b987ed207c648eb5d56930b21f745440dd3ce16a596088e37b40"},"schema_version":"1.0","source":{"id":"2202.05245","kind":"arxiv","version":2}},"canonical_sha256":"16fc814c4772ce195379bc231b41e0773307265654adad6add70bd838935ab77","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"16fc814c4772ce195379bc231b41e0773307265654adad6add70bd838935ab77","first_computed_at":"2026-07-05T03:57:31.982365Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:57:31.982365Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jgKQ84fys5bXQVZiLZwmRh91A/AkNRWgL5KmCb/2lYSAe+/Bdsw+2sRvrHlcoWf7/WdHm4b5SgalQe+mUU+3BA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:57:31.982828Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.05245","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:865577241f2cbde3b864ef73b861a74b25f53a791c228f9d24fd275f59eb4206","sha256:cfee299088af9c47bce72953ba77de415478608c5ff95e07b3def8cc2ade5839"],"state_sha256":"5cf708e58924c442c61afcd1547b48d021c926d615cf653cfff97717a264f710"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t7Byoazb1fbvz548zjyD1IlhDBLBCSckagG1a14J7RjCWe1oqzIK5CR6sjBslYh0psBGSE+l/iWu/TWCLB/tBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:34:11.043093Z","bundle_sha256":"b097fe2bfe7c6ffe48aced664daf7535fa657b450cbbfb49c53cc99d16868b95"}}