{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2IT3IL52NUC64RPKVI6HBQV6SP","short_pith_number":"pith:2IT3IL52","schema_version":"1.0","canonical_sha256":"d227b42fba6d05ee45eaaa3c70c2be93f77727dec3284b82fba1905b4b775d55","source":{"kind":"arxiv","id":"2102.11076","version":4},"attestation_state":"computed","paper":{"title":"Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","econ.EM","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Rahul Singh","submitted_at":"2021-02-22T14:46:23Z","abstract_excerpt":"Kernel balancing weights provide confidence intervals for average treatment effects, based on the idea of balancing covariates for the treated group and untreated group in feature space, often with ridge regularization. Previous works on the classical kernel ridge balancing weights have certain limitations: (i) not articulating generalization error for the balancing weights, (ii) typically requiring correct specification of features, and (iii) justifying Gaussian approximation for only average effects.\n  I interpret kernel balancing weights as kernel ridge Riesz representers (KRRR) and address"},"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":"2102.11076","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2021-02-22T14:46:23Z","cross_cats_sorted":["cs.LG","econ.EM","math.ST","stat.TH"],"title_canon_sha256":"e9ad604225e3966c11982611d053b7bbea2929fe3fb12a635506584d050cd093","abstract_canon_sha256":"8333a83a144d3653f2587e455fe2d5204455497bf78a66ad9e21fa171258de21"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:40:02.853906Z","signature_b64":"yuIS0z9rUJX51FI6H9mbQRQYKAXBahDIlG4qhf0AFcMDM18r94gTKz6bIRWJSir1ZEkXkWnEZ5dlGnuQhAVWDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d227b42fba6d05ee45eaaa3c70c2be93f77727dec3284b82fba1905b4b775d55","last_reissued_at":"2026-07-05T08:40:02.853424Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:40:02.853424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","econ.EM","math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Rahul Singh","submitted_at":"2021-02-22T14:46:23Z","abstract_excerpt":"Kernel balancing weights provide confidence intervals for average treatment effects, based on the idea of balancing covariates for the treated group and untreated group in feature space, often with ridge regularization. Previous works on the classical kernel ridge balancing weights have certain limitations: (i) not articulating generalization error for the balancing weights, (ii) typically requiring correct specification of features, and (iii) justifying Gaussian approximation for only average effects.\n  I interpret kernel balancing weights as kernel ridge Riesz representers (KRRR) and address"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.11076","kind":"arxiv","version":4},"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/2102.11076/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":"2102.11076","created_at":"2026-07-05T08:40:02.853484+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.11076v4","created_at":"2026-07-05T08:40:02.853484+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.11076","created_at":"2026-07-05T08:40:02.853484+00:00"},{"alias_kind":"pith_short_12","alias_value":"2IT3IL52NUC6","created_at":"2026-07-05T08:40:02.853484+00:00"},{"alias_kind":"pith_short_16","alias_value":"2IT3IL52NUC64RPK","created_at":"2026-07-05T08:40:02.853484+00:00"},{"alias_kind":"pith_short_8","alias_value":"2IT3IL52","created_at":"2026-07-05T08:40:02.853484+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29009","citing_title":"Generated outcomes as generated regressors: Equivalences in recursive causal estimation","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP","json":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP.json","graph_json":"https://pith.science/api/pith-number/2IT3IL52NUC64RPKVI6HBQV6SP/graph.json","events_json":"https://pith.science/api/pith-number/2IT3IL52NUC64RPKVI6HBQV6SP/events.json","paper":"https://pith.science/paper/2IT3IL52"},"agent_actions":{"view_html":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP","download_json":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP.json","view_paper":"https://pith.science/paper/2IT3IL52","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.11076&json=true","fetch_graph":"https://pith.science/api/pith-number/2IT3IL52NUC64RPKVI6HBQV6SP/graph.json","fetch_events":"https://pith.science/api/pith-number/2IT3IL52NUC64RPKVI6HBQV6SP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP/action/storage_attestation","attest_author":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP/action/author_attestation","sign_citation":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP/action/citation_signature","submit_replication":"https://pith.science/pith/2IT3IL52NUC64RPKVI6HBQV6SP/action/replication_record"}},"created_at":"2026-07-05T08:40:02.853484+00:00","updated_at":"2026-07-05T08:40:02.853484+00:00"}