{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3LW2D44OURXSNGCLBFKWPX7MTG","short_pith_number":"pith:3LW2D44O","schema_version":"1.0","canonical_sha256":"daeda1f38ea46f26984b095567dfec9985d1db4defba2c115fc548b019af3546","source":{"kind":"arxiv","id":"2506.12765","version":3},"attestation_state":"computed","paper":{"title":"Model Risk in Machine-Learning Distributional IV Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Charles Shaw","submitted_at":"2025-06-15T08:08:33Z","abstract_excerpt":"We study model risk in machine-learning estimation of the Distributional Instrumental Variable Local Average Treatment Effect (D-IV-LATE), the distributional IV effect for the subpopulation induced into treatment by the instrument. The contribution is not a new neural causal estimand. We implement a reduced-form orthogonal level-score DML estimator for the covariate-adjusted D-IV-LATE target and use it to ask how much the nuisance learner matters for distributional IV conclusions.\n  In simulations with explicit monotone principal strata and known complier truth, Kolmogorov-Arnold Networks (KAN"},"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":"2506.12765","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2025-06-15T08:08:33Z","cross_cats_sorted":[],"title_canon_sha256":"13986eb703b45b7dde86144238eb1fc9bad85b416620b65518553b30564fe3aa","abstract_canon_sha256":"5974d5d26316a1d509c130ab085bc4270f981aec704b82e6af352bab650fb6da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:07.197512Z","signature_b64":"zjZhputfujqe18UZgGYvzjaedQ5cN7AO4KUklY6gSNJICHKdKuCaa67yWklh0v16ID+cLVYAtQ8w8cww5bxGCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"daeda1f38ea46f26984b095567dfec9985d1db4defba2c115fc548b019af3546","last_reissued_at":"2026-06-09T01:05:07.196828Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:07.196828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Model Risk in Machine-Learning Distributional IV Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Charles Shaw","submitted_at":"2025-06-15T08:08:33Z","abstract_excerpt":"We study model risk in machine-learning estimation of the Distributional Instrumental Variable Local Average Treatment Effect (D-IV-LATE), the distributional IV effect for the subpopulation induced into treatment by the instrument. The contribution is not a new neural causal estimand. We implement a reduced-form orthogonal level-score DML estimator for the covariate-adjusted D-IV-LATE target and use it to ask how much the nuisance learner matters for distributional IV conclusions.\n  In simulations with explicit monotone principal strata and known complier truth, Kolmogorov-Arnold Networks (KAN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12765","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/2506.12765/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":"2506.12765","created_at":"2026-06-09T01:05:07.196896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12765v3","created_at":"2026-06-09T01:05:07.196896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12765","created_at":"2026-06-09T01:05:07.196896+00:00"},{"alias_kind":"pith_short_12","alias_value":"3LW2D44OURXS","created_at":"2026-06-09T01:05:07.196896+00:00"},{"alias_kind":"pith_short_16","alias_value":"3LW2D44OURXSNGCL","created_at":"2026-06-09T01:05:07.196896+00:00"},{"alias_kind":"pith_short_8","alias_value":"3LW2D44O","created_at":"2026-06-09T01:05:07.196896+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/3LW2D44OURXSNGCLBFKWPX7MTG","json":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG.json","graph_json":"https://pith.science/api/pith-number/3LW2D44OURXSNGCLBFKWPX7MTG/graph.json","events_json":"https://pith.science/api/pith-number/3LW2D44OURXSNGCLBFKWPX7MTG/events.json","paper":"https://pith.science/paper/3LW2D44O"},"agent_actions":{"view_html":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG","download_json":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG.json","view_paper":"https://pith.science/paper/3LW2D44O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12765&json=true","fetch_graph":"https://pith.science/api/pith-number/3LW2D44OURXSNGCLBFKWPX7MTG/graph.json","fetch_events":"https://pith.science/api/pith-number/3LW2D44OURXSNGCLBFKWPX7MTG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG/action/storage_attestation","attest_author":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG/action/author_attestation","sign_citation":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG/action/citation_signature","submit_replication":"https://pith.science/pith/3LW2D44OURXSNGCLBFKWPX7MTG/action/replication_record"}},"created_at":"2026-06-09T01:05:07.196896+00:00","updated_at":"2026-06-09T01:05:07.196896+00:00"}