{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:5PPLZVJ6V7J3QMKIYDQCBKIS7Y","short_pith_number":"pith:5PPLZVJ6","canonical_record":{"source":{"id":"2005.03447","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T00:28:18Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"4b59c2eca8368ae01ab66bb4b931cec02a7c28ead535050f3be9dcdad487ae6e","abstract_canon_sha256":"8a0a1323949bd5bd982ba9e16d96db2e8a2e23f13db6dbac0b21ab8e3ec153f3"},"schema_version":"1.0"},"canonical_sha256":"ebdebcd53eafd3b83148c0e020a912fe0326e43c2e91c8ec2cb9fef25bacc426","source":{"kind":"arxiv","id":"2005.03447","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.03447","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"arxiv_version","alias_value":"2005.03447v2","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.03447","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"pith_short_12","alias_value":"5PPLZVJ6V7J3","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"pith_short_16","alias_value":"5PPLZVJ6V7J3QMKI","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"pith_short_8","alias_value":"5PPLZVJ6","created_at":"2026-07-05T04:40:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:5PPLZVJ6V7J3QMKIYDQCBKIS7Y","target":"record","payload":{"canonical_record":{"source":{"id":"2005.03447","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T00:28:18Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"4b59c2eca8368ae01ab66bb4b931cec02a7c28ead535050f3be9dcdad487ae6e","abstract_canon_sha256":"8a0a1323949bd5bd982ba9e16d96db2e8a2e23f13db6dbac0b21ab8e3ec153f3"},"schema_version":"1.0"},"canonical_sha256":"ebdebcd53eafd3b83148c0e020a912fe0326e43c2e91c8ec2cb9fef25bacc426","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:40:05.844399Z","signature_b64":"VCxiNc0I/Bmj8X+bJfkqaWozRE0JZ/sA1AeFCf+0FIq4bzCllovVvaloTf5fAA65SNppbxuX3zVnjUQor03ZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebdebcd53eafd3b83148c0e020a912fe0326e43c2e91c8ec2cb9fef25bacc426","last_reissued_at":"2026-07-05T04:40:05.843996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:40:05.843996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.03447","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":"2026-07-05T04:40:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+urlMt5o+1C0NTsShJqN8pcYN66bEDOgIOGHSKPIUhWc+ergzqmrWXDKtraXRh1jJIV6BYosF30RFfXvoXieCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T07:17:46.370725Z"},"content_sha256":"71c53309111a7d54c0ab1fb391c4434d5a8309aa4df097527fdab9afa9d1d724","schema_version":"1.0","event_id":"sha256:71c53309111a7d54c0ab1fb391c4434d5a8309aa4df097527fdab9afa9d1d724"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:5PPLZVJ6V7J3QMKIYDQCBKIS7Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Mike Yung, Totte Harinen, Yumin Zhang, Zhenyu Zhao","submitted_at":"2020-05-05T00:28:18Z","abstract_excerpt":"Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for tasks such as targeting ads. In a typical setting, uplift models can take thousands of features as inputs, which is costly and results in problems such as overfitting and poor model interpretability. Consequently, there is a need to select a subset of the most important features for modeling. However, traditional methods for doing feature selection are not fit for the task because they are designed for standard machine learning models whose target is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.03447","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/2005.03447/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":"2026-07-05T04:40:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7pFUWPg570Wrn2bicKb8mn9XfXR4QjfCZClVsAi6f/AemyHe7YXqntjHE/iGL3SIS0cUZUs52vAVIrSYTlArDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T07:17:46.371109Z"},"content_sha256":"3f34d5e240c9e241d20bbfa30d86ee4dad729b86f266c4ccc4bbae72da620778","schema_version":"1.0","event_id":"sha256:3f34d5e240c9e241d20bbfa30d86ee4dad729b86f266c4ccc4bbae72da620778"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5PPLZVJ6V7J3QMKIYDQCBKIS7Y/bundle.json","state_url":"https://pith.science/pith/5PPLZVJ6V7J3QMKIYDQCBKIS7Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5PPLZVJ6V7J3QMKIYDQCBKIS7Y/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":"2026-07-08T07:17:46Z","links":{"resolver":"https://pith.science/pith/5PPLZVJ6V7J3QMKIYDQCBKIS7Y","bundle":"https://pith.science/pith/5PPLZVJ6V7J3QMKIYDQCBKIS7Y/bundle.json","state":"https://pith.science/pith/5PPLZVJ6V7J3QMKIYDQCBKIS7Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5PPLZVJ6V7J3QMKIYDQCBKIS7Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:5PPLZVJ6V7J3QMKIYDQCBKIS7Y","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":"8a0a1323949bd5bd982ba9e16d96db2e8a2e23f13db6dbac0b21ab8e3ec153f3","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T00:28:18Z","title_canon_sha256":"4b59c2eca8368ae01ab66bb4b931cec02a7c28ead535050f3be9dcdad487ae6e"},"schema_version":"1.0","source":{"id":"2005.03447","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.03447","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"arxiv_version","alias_value":"2005.03447v2","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.03447","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"pith_short_12","alias_value":"5PPLZVJ6V7J3","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"pith_short_16","alias_value":"5PPLZVJ6V7J3QMKI","created_at":"2026-07-05T04:40:05Z"},{"alias_kind":"pith_short_8","alias_value":"5PPLZVJ6","created_at":"2026-07-05T04:40:05Z"}],"graph_snapshots":[{"event_id":"sha256:3f34d5e240c9e241d20bbfa30d86ee4dad729b86f266c4ccc4bbae72da620778","target":"graph","created_at":"2026-07-05T04:40:05Z","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/2005.03447/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for tasks such as targeting ads. In a typical setting, uplift models can take thousands of features as inputs, which is costly and results in problems such as overfitting and poor model interpretability. Consequently, there is a need to select a subset of the most important features for modeling. However, traditional methods for doing feature selection are not fit for the task because they are designed for standard machine learning models whose target is","authors_text":"Mike Yung, Totte Harinen, Yumin Zhang, Zhenyu Zhao","cross_cats":["stat.ME","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T00:28:18Z","title":"Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.03447","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":"sha256:71c53309111a7d54c0ab1fb391c4434d5a8309aa4df097527fdab9afa9d1d724","target":"record","created_at":"2026-07-05T04:40:05Z","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":"8a0a1323949bd5bd982ba9e16d96db2e8a2e23f13db6dbac0b21ab8e3ec153f3","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-05-05T00:28:18Z","title_canon_sha256":"4b59c2eca8368ae01ab66bb4b931cec02a7c28ead535050f3be9dcdad487ae6e"},"schema_version":"1.0","source":{"id":"2005.03447","kind":"arxiv","version":2}},"canonical_sha256":"ebdebcd53eafd3b83148c0e020a912fe0326e43c2e91c8ec2cb9fef25bacc426","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebdebcd53eafd3b83148c0e020a912fe0326e43c2e91c8ec2cb9fef25bacc426","first_computed_at":"2026-07-05T04:40:05.843996Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:40:05.843996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VCxiNc0I/Bmj8X+bJfkqaWozRE0JZ/sA1AeFCf+0FIq4bzCllovVvaloTf5fAA65SNppbxuX3zVnjUQor03ZAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:40:05.844399Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.03447","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:71c53309111a7d54c0ab1fb391c4434d5a8309aa4df097527fdab9afa9d1d724","sha256:3f34d5e240c9e241d20bbfa30d86ee4dad729b86f266c4ccc4bbae72da620778"],"state_sha256":"a875a7a208c5767078af035daf4c4e7838a1daef06020903c96904f05b5e244e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F2SLaR/T/1Jk/o6iyKE1GP/5ubwraggt+nTY0fyShnESdsEewbb+8D5fNRwJY1k3BbG+wfQKLc5yrEj2tfSTBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T07:17:46.373603Z","bundle_sha256":"b17966a4467ba911eb4dbf3d7e779a40aa44013d575177a4c146115cca41c12e"}}