{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:J3BUNBBJIAM3LTXY44Q77CSDLJ","short_pith_number":"pith:J3BUNBBJ","schema_version":"1.0","canonical_sha256":"4ec34684294019b5cef8e721ff8a435a6b104eabce57fef86f87261ad51755c1","source":{"kind":"arxiv","id":"1905.10176","version":3},"attestation_state":"computed","paper":{"title":"Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.ML"],"primary_cat":"econ.EM","authors_text":"Greg Lewis, Keith Battocchi, Maggie Hei, Miruna Oprescu, Vasilis Syrgkanis, Victor Lei","submitted_at":"2019-05-24T12:14:08Z","abstract_excerpt":"We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxili"},"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":"1905.10176","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2019-05-24T12:14:08Z","cross_cats_sorted":["cs.LG","stat.AP","stat.ML"],"title_canon_sha256":"0068625b667e612ffdbc1da76a255219a5c4fda7e3c924876a8909223e40fd09","abstract_canon_sha256":"80bd1457d1cb24499a35016214281b81024ba96f9090376310b642962aa464e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:02.102989Z","signature_b64":"sfBb9P1/d9LRbCZhiTNB/+G9GNk92wHHGhFy/QyDuEl0X/RbNC53fCOw86p9HYnZkywDWZLzK1DgrYAITI/eCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ec34684294019b5cef8e721ff8a435a6b104eabce57fef86f87261ad51755c1","last_reissued_at":"2026-05-17T23:44:02.102549Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:02.102549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.ML"],"primary_cat":"econ.EM","authors_text":"Greg Lewis, Keith Battocchi, Maggie Hei, Miruna Oprescu, Vasilis Syrgkanis, Victor Lei","submitted_at":"2019-05-24T12:14:08Z","abstract_excerpt":"We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where the experimenter randomizes over which user will receive a recommendation to take an action, and we are interested in the effect of the downstream action. We develop a statistical learning approach to the estimation of heterogeneous effects, reducing the problem to the minimization of an appropriate loss function that depends on a set of auxili"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10176","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":""},"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":"1905.10176","created_at":"2026-05-17T23:44:02.102620+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.10176v3","created_at":"2026-05-17T23:44:02.102620+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10176","created_at":"2026-05-17T23:44:02.102620+00:00"},{"alias_kind":"pith_short_12","alias_value":"J3BUNBBJIAM3","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"J3BUNBBJIAM3LTXY","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"J3BUNBBJ","created_at":"2026-05-18T12:33:18.533446+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/J3BUNBBJIAM3LTXY44Q77CSDLJ","json":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ.json","graph_json":"https://pith.science/api/pith-number/J3BUNBBJIAM3LTXY44Q77CSDLJ/graph.json","events_json":"https://pith.science/api/pith-number/J3BUNBBJIAM3LTXY44Q77CSDLJ/events.json","paper":"https://pith.science/paper/J3BUNBBJ"},"agent_actions":{"view_html":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ","download_json":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ.json","view_paper":"https://pith.science/paper/J3BUNBBJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.10176&json=true","fetch_graph":"https://pith.science/api/pith-number/J3BUNBBJIAM3LTXY44Q77CSDLJ/graph.json","fetch_events":"https://pith.science/api/pith-number/J3BUNBBJIAM3LTXY44Q77CSDLJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ/action/storage_attestation","attest_author":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ/action/author_attestation","sign_citation":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ/action/citation_signature","submit_replication":"https://pith.science/pith/J3BUNBBJIAM3LTXY44Q77CSDLJ/action/replication_record"}},"created_at":"2026-05-17T23:44:02.102620+00:00","updated_at":"2026-05-17T23:44:02.102620+00:00"}