{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AH44TZGJQ33RRERTLC7YMFKERQ","short_pith_number":"pith:AH44TZGJ","schema_version":"1.0","canonical_sha256":"01f9c9e4c986f718923358bf8615448c331ba04127257e3da2cdc99ad2961d6d","source":{"kind":"arxiv","id":"2212.09931","version":3},"attestation_state":"computed","paper":{"title":"A Generalized Variable Importance Metric and Estimator for Black Box Machine Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Mohammad Kaviul Anam Khan, Olli Saarela, Rafal Kustra","submitted_at":"2022-12-20T00:50:28Z","abstract_excerpt":"In this paper we define a population parameter, ``Generalized Variable Importance Metric (GVIM)'', to measure importance of predictors for black box machine learning methods, where the importance is not represented by model-based parameter. GVIM is defined for each input variable, using the true conditional expectation function, and it measures the variable's importance in affecting a continuous or a binary response. We extend previously published results to show that the defined GVIM can be represented as a function of the Conditional Average Treatment Effect (CATE) for any kind of a predicto"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2212.09931","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-12-20T00:50:28Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"82606ac3d93cf3cdf73ddb171c8a56f876ca6e5c115ce5a293f37c85b5acf4f3","abstract_canon_sha256":"5fea96dc8f59e3c82516c5086a4874e4ecaf04d8412336ba3cf0c3cc9771736c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:23.392867Z","signature_b64":"xyEmYM0otRO18WkGE2jD3SoE9muO5LxVGXAF/oB/y1cxLX9WBCI1+BCZs3XyFT6Fa3kXxNQgHye2TB96HQVaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01f9c9e4c986f718923358bf8615448c331ba04127257e3da2cdc99ad2961d6d","last_reissued_at":"2026-07-05T07:27:23.392360Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:23.392360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Generalized Variable Importance Metric and Estimator for Black Box Machine Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.CO","authors_text":"Mohammad Kaviul Anam Khan, Olli Saarela, Rafal Kustra","submitted_at":"2022-12-20T00:50:28Z","abstract_excerpt":"In this paper we define a population parameter, ``Generalized Variable Importance Metric (GVIM)'', to measure importance of predictors for black box machine learning methods, where the importance is not represented by model-based parameter. GVIM is defined for each input variable, using the true conditional expectation function, and it measures the variable's importance in affecting a continuous or a binary response. We extend previously published results to show that the defined GVIM can be represented as a function of the Conditional Average Treatment Effect (CATE) for any kind of a predicto"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09931","kind":"arxiv","version":3},"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/2212.09931/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2212.09931","created_at":"2026-07-05T07:27:23.392437+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09931v3","created_at":"2026-07-05T07:27:23.392437+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09931","created_at":"2026-07-05T07:27:23.392437+00:00"},{"alias_kind":"pith_short_12","alias_value":"AH44TZGJQ33R","created_at":"2026-07-05T07:27:23.392437+00:00"},{"alias_kind":"pith_short_16","alias_value":"AH44TZGJQ33RRERT","created_at":"2026-07-05T07:27:23.392437+00:00"},{"alias_kind":"pith_short_8","alias_value":"AH44TZGJ","created_at":"2026-07-05T07:27:23.392437+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ","json":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ.json","graph_json":"https://pith.science/api/pith-number/AH44TZGJQ33RRERTLC7YMFKERQ/graph.json","events_json":"https://pith.science/api/pith-number/AH44TZGJQ33RRERTLC7YMFKERQ/events.json","paper":"https://pith.science/paper/AH44TZGJ"},"agent_actions":{"view_html":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ","download_json":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ.json","view_paper":"https://pith.science/paper/AH44TZGJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09931&json=true","fetch_graph":"https://pith.science/api/pith-number/AH44TZGJQ33RRERTLC7YMFKERQ/graph.json","fetch_events":"https://pith.science/api/pith-number/AH44TZGJQ33RRERTLC7YMFKERQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ/action/storage_attestation","attest_author":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ/action/author_attestation","sign_citation":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ/action/citation_signature","submit_replication":"https://pith.science/pith/AH44TZGJQ33RRERTLC7YMFKERQ/action/replication_record"}},"created_at":"2026-07-05T07:27:23.392437+00:00","updated_at":"2026-07-05T07:27:23.392437+00:00"}