{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:BAASIA42K63A6WUS4DDKQF256K","short_pith_number":"pith:BAASIA42","schema_version":"1.0","canonical_sha256":"080124039a57b60f5a92e0c6a8175df29419eb523fec1100a20023cf2e616f32","source":{"kind":"arxiv","id":"2010.14694","version":4},"attestation_state":"computed","paper":{"title":"Deep Learning for Individual Heterogeneity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"econ.EM","authors_text":"Max H. Farrell, Sanjog Misra, Tengyuan Liang","submitted_at":"2020-10-28T01:41:47Z","abstract_excerpt":"This paper integrates deep neural networks (DNNs) into structural models to increase flexibility and capture rich heterogeneity while preserving interpretability. Economic (or scientific or domain-restricted) structure and machine learning are complements in empirical modeling, not substitutes: DNNs provide the capacity to learn complex, nonlinear heterogeneity, while the structure ensures the estimates remain interpretable and suitable for decision-making and policy analysis. We start with a standard parametric structural model and then enrich its parameters into fully flexible functions, whi"},"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":"2010.14694","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2020-10-28T01:41:47Z","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"ae34641822d77f309088ab8925eeef16b9cd5f5adc7d7c0d1c79016cd5b1c30f","abstract_canon_sha256":"c8e3d923a46a4f4b177b5af4c54d018ca1b723cc069ba89ab261835c5d0a3c1d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:13:41.992777Z","signature_b64":"EfaaSekTHl9wcaNppEwpdLo+s2r+paZtt5j07CdeHMiENlRrl0x9WDmYtrcaM9adaRQnNmRzZbSe4LpvA2s8AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"080124039a57b60f5a92e0c6a8175df29419eb523fec1100a20023cf2e616f32","last_reissued_at":"2026-06-23T03:13:41.992244Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:13:41.992244Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Individual Heterogeneity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"econ.EM","authors_text":"Max H. Farrell, Sanjog Misra, Tengyuan Liang","submitted_at":"2020-10-28T01:41:47Z","abstract_excerpt":"This paper integrates deep neural networks (DNNs) into structural models to increase flexibility and capture rich heterogeneity while preserving interpretability. Economic (or scientific or domain-restricted) structure and machine learning are complements in empirical modeling, not substitutes: DNNs provide the capacity to learn complex, nonlinear heterogeneity, while the structure ensures the estimates remain interpretable and suitable for decision-making and policy analysis. We start with a standard parametric structural model and then enrich its parameters into fully flexible functions, whi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.14694","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/2010.14694/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":"2010.14694","created_at":"2026-06-23T03:13:41.992302+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.14694v4","created_at":"2026-06-23T03:13:41.992302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.14694","created_at":"2026-06-23T03:13:41.992302+00:00"},{"alias_kind":"pith_short_12","alias_value":"BAASIA42K63A","created_at":"2026-06-23T03:13:41.992302+00:00"},{"alias_kind":"pith_short_16","alias_value":"BAASIA42K63A6WUS","created_at":"2026-06-23T03:13:41.992302+00:00"},{"alias_kind":"pith_short_8","alias_value":"BAASIA42","created_at":"2026-06-23T03:13:41.992302+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2606.07483","citing_title":"Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10845","citing_title":"Learning Preferences from Conjoint Data: A Hybrid Structural Deep Learning Approach","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K","json":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K.json","graph_json":"https://pith.science/api/pith-number/BAASIA42K63A6WUS4DDKQF256K/graph.json","events_json":"https://pith.science/api/pith-number/BAASIA42K63A6WUS4DDKQF256K/events.json","paper":"https://pith.science/paper/BAASIA42"},"agent_actions":{"view_html":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K","download_json":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K.json","view_paper":"https://pith.science/paper/BAASIA42","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.14694&json=true","fetch_graph":"https://pith.science/api/pith-number/BAASIA42K63A6WUS4DDKQF256K/graph.json","fetch_events":"https://pith.science/api/pith-number/BAASIA42K63A6WUS4DDKQF256K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K/action/storage_attestation","attest_author":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K/action/author_attestation","sign_citation":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K/action/citation_signature","submit_replication":"https://pith.science/pith/BAASIA42K63A6WUS4DDKQF256K/action/replication_record"}},"created_at":"2026-06-23T03:13:41.992302+00:00","updated_at":"2026-06-23T03:13:41.992302+00:00"}