{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:76P6WZGL7LA6U5DGOQH73U2TE6","short_pith_number":"pith:76P6WZGL","schema_version":"1.0","canonical_sha256":"ff9feb64cbfac1ea7466740ffdd3532781c136f9ec6777dba57f44d5cfc67166","source":{"kind":"arxiv","id":"2110.03031","version":3},"attestation_state":"computed","paper":{"title":"RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.ML"],"primary_cat":"cs.LG","authors_text":"Vasilis Syrgkanis, Victor Chernozhukov, Victor Quintas-Martinez, Whitney K. Newey","submitted_at":"2021-10-06T19:29:20Z","abstract_excerpt":"Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. $\\sqrt{n}$-consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce the effects of regularization and/or model selection on the object of interest. Debiasing is typically achieved by adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality. We implement an automatic debiasing procedure based on aut"},"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":"2110.03031","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-06T19:29:20Z","cross_cats_sorted":["econ.EM","stat.ML"],"title_canon_sha256":"4fa603526afaf4761346cad75957f2fd5e508b5716d7b1c60242822c68c33d7e","abstract_canon_sha256":"381c62cba0ce9ce5065f950bd1742ad65d87d2d734b95129a1faa5cf6e4fb233"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:31:57.491979Z","signature_b64":"LYHGb38HNVQ0DL/2FoKeydLkPc+fnU0+Ifq13PabbAgK5XpKXSEtm5EN1FU/0v+Fpg2eJEJJmzr10hhOzEJqCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff9feb64cbfac1ea7466740ffdd3532781c136f9ec6777dba57f44d5cfc67166","last_reissued_at":"2026-07-05T04:31:57.491360Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:31:57.491360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["econ.EM","stat.ML"],"primary_cat":"cs.LG","authors_text":"Vasilis Syrgkanis, Victor Chernozhukov, Victor Quintas-Martinez, Whitney K. Newey","submitted_at":"2021-10-06T19:29:20Z","abstract_excerpt":"Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. $\\sqrt{n}$-consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce the effects of regularization and/or model selection on the object of interest. Debiasing is typically achieved by adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality. We implement an automatic debiasing procedure based on aut"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.03031","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/2110.03031/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":"2110.03031","created_at":"2026-07-05T04:31:57.491443+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.03031v3","created_at":"2026-07-05T04:31:57.491443+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.03031","created_at":"2026-07-05T04:31:57.491443+00:00"},{"alias_kind":"pith_short_12","alias_value":"76P6WZGL7LA6","created_at":"2026-07-05T04:31:57.491443+00:00"},{"alias_kind":"pith_short_16","alias_value":"76P6WZGL7LA6U5DG","created_at":"2026-07-05T04:31:57.491443+00:00"},{"alias_kind":"pith_short_8","alias_value":"76P6WZGL","created_at":"2026-07-05T04:31:57.491443+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.12435","citing_title":"Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6","json":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6.json","graph_json":"https://pith.science/api/pith-number/76P6WZGL7LA6U5DGOQH73U2TE6/graph.json","events_json":"https://pith.science/api/pith-number/76P6WZGL7LA6U5DGOQH73U2TE6/events.json","paper":"https://pith.science/paper/76P6WZGL"},"agent_actions":{"view_html":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6","download_json":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6.json","view_paper":"https://pith.science/paper/76P6WZGL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.03031&json=true","fetch_graph":"https://pith.science/api/pith-number/76P6WZGL7LA6U5DGOQH73U2TE6/graph.json","fetch_events":"https://pith.science/api/pith-number/76P6WZGL7LA6U5DGOQH73U2TE6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6/action/storage_attestation","attest_author":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6/action/author_attestation","sign_citation":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6/action/citation_signature","submit_replication":"https://pith.science/pith/76P6WZGL7LA6U5DGOQH73U2TE6/action/replication_record"}},"created_at":"2026-07-05T04:31:57.491443+00:00","updated_at":"2026-07-05T04:31:57.491443+00:00"}