{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:3F2FGO3BPVJTMYSB2DK4MDXMBW","short_pith_number":"pith:3F2FGO3B","schema_version":"1.0","canonical_sha256":"d974533b617d53366241d0d5c60eec0daed80e10ecd9d184b38eff4ccd9a2c6d","source":{"kind":"arxiv","id":"2012.10315","version":5},"attestation_state":"computed","paper":{"title":"Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","econ.EM"],"primary_cat":"stat.ML","authors_text":"Rahul Singh","submitted_at":"2020-12-18T16:00:08Z","abstract_excerpt":"Negative control is a strategy for learning the causal relationship between treatment and outcome in the presence of unmeasured confounding. The treatment effect can nonetheless be identified if two auxiliary variables are available: a negative control treatment (which has no effect on the actual outcome), and a negative control outcome (which is not affected by the actual treatment). These auxiliary variables can also be viewed as proxies for a traditional set of control variables, and they bear resemblance to instrumental variables. I propose a family of algorithms based on kernel ridge regr"},"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":"2012.10315","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-12-18T16:00:08Z","cross_cats_sorted":["cs.LG","econ.EM"],"title_canon_sha256":"4de19fbcdba63be543c4168f4490d96a0b884d41720654a3f00085b00456adbe","abstract_canon_sha256":"ec060f4c67c9b1ba3cbd961181f687b0b9c479c79db003252e28a874e1021044"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:53:46.041763Z","signature_b64":"0jp7Pg1P1ieLbKAx3FNg3rQ6+OLwKqQzac6eUzzsBjpvIESfm/VnxbCZkmuxUu9jqLxKXVJcIV7d3f4QL6JuCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d974533b617d53366241d0d5c60eec0daed80e10ecd9d184b38eff4ccd9a2c6d","last_reissued_at":"2026-07-05T05:53:46.041423Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:53:46.041423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","econ.EM"],"primary_cat":"stat.ML","authors_text":"Rahul Singh","submitted_at":"2020-12-18T16:00:08Z","abstract_excerpt":"Negative control is a strategy for learning the causal relationship between treatment and outcome in the presence of unmeasured confounding. The treatment effect can nonetheless be identified if two auxiliary variables are available: a negative control treatment (which has no effect on the actual outcome), and a negative control outcome (which is not affected by the actual treatment). These auxiliary variables can also be viewed as proxies for a traditional set of control variables, and they bear resemblance to instrumental variables. I propose a family of algorithms based on kernel ridge regr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.10315","kind":"arxiv","version":5},"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/2012.10315/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":"2012.10315","created_at":"2026-07-05T05:53:46.041478+00:00"},{"alias_kind":"arxiv_version","alias_value":"2012.10315v5","created_at":"2026-07-05T05:53:46.041478+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.10315","created_at":"2026-07-05T05:53:46.041478+00:00"},{"alias_kind":"pith_short_12","alias_value":"3F2FGO3BPVJT","created_at":"2026-07-05T05:53:46.041478+00:00"},{"alias_kind":"pith_short_16","alias_value":"3F2FGO3BPVJTMYSB","created_at":"2026-07-05T05:53:46.041478+00:00"},{"alias_kind":"pith_short_8","alias_value":"3F2FGO3B","created_at":"2026-07-05T05:53:46.041478+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09514","citing_title":"Doubly Robust Proxy Causal Learning with Neural Mean Embeddings","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09257","citing_title":"Regularity, Phase Transitions, and Uniform Inference for Proximal Counterfactual Quantile Processes","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW","json":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW.json","graph_json":"https://pith.science/api/pith-number/3F2FGO3BPVJTMYSB2DK4MDXMBW/graph.json","events_json":"https://pith.science/api/pith-number/3F2FGO3BPVJTMYSB2DK4MDXMBW/events.json","paper":"https://pith.science/paper/3F2FGO3B"},"agent_actions":{"view_html":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW","download_json":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW.json","view_paper":"https://pith.science/paper/3F2FGO3B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2012.10315&json=true","fetch_graph":"https://pith.science/api/pith-number/3F2FGO3BPVJTMYSB2DK4MDXMBW/graph.json","fetch_events":"https://pith.science/api/pith-number/3F2FGO3BPVJTMYSB2DK4MDXMBW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW/action/storage_attestation","attest_author":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW/action/author_attestation","sign_citation":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW/action/citation_signature","submit_replication":"https://pith.science/pith/3F2FGO3BPVJTMYSB2DK4MDXMBW/action/replication_record"}},"created_at":"2026-07-05T05:53:46.041478+00:00","updated_at":"2026-07-05T05:53:46.041478+00:00"}