{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EUVYO5DT2FSW4TLK6ZWPZCRS26","short_pith_number":"pith:EUVYO5DT","schema_version":"1.0","canonical_sha256":"252b877473d1656e4d6af66cfc8a32d7949247e802b792f52957746125f4ca77","source":{"kind":"arxiv","id":"2409.04874","version":2},"attestation_state":"computed","paper":{"title":"Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"econ.EM","authors_text":"Daniele Ballinari, Nora Bearth","submitted_at":"2024-09-07T17:44:01Z","abstract_excerpt":"In the last decade, machine learning techniques have gained popularity for estimating causal effects. One machine learning approach that can be used for estimating an average treatment effect is Double/debiased machine learning (DML) (Chernozhukov et al., 2018). This approach uses a double-robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment conditional on covariates. Estimators relying on double-robust score functions are highly sensitive to errors in propensity score predictions. Machine lear"},"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":"2409.04874","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"econ.EM","submitted_at":"2024-09-07T17:44:01Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"a95a506e42e73f37524e4ba15eb5d77bf309e03c219327246886456da309d9c9","abstract_canon_sha256":"a5cb9942231a9ae976e9b86cd55aeae8a8568b8e6adb57c9bc97c42991721641"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:01:36.229799Z","signature_b64":"N8Q/f2aVSaWhWYGnOhWQ2BESU3Fi9JabUFWkMW+xj5Aoe1yvF1FXUvUt0GQd9Gxe6Qn8wy+OdXHBuh9yvw46Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"252b877473d1656e4d6af66cfc8a32d7949247e802b792f52957746125f4ca77","last_reissued_at":"2026-07-05T10:01:36.229254Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:01:36.229254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"econ.EM","authors_text":"Daniele Ballinari, Nora Bearth","submitted_at":"2024-09-07T17:44:01Z","abstract_excerpt":"In the last decade, machine learning techniques have gained popularity for estimating causal effects. One machine learning approach that can be used for estimating an average treatment effect is Double/debiased machine learning (DML) (Chernozhukov et al., 2018). This approach uses a double-robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment conditional on covariates. Estimators relying on double-robust score functions are highly sensitive to errors in propensity score predictions. Machine lear"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.04874","kind":"arxiv","version":2},"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/2409.04874/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":"2409.04874","created_at":"2026-07-05T10:01:36.229312+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.04874v2","created_at":"2026-07-05T10:01:36.229312+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.04874","created_at":"2026-07-05T10:01:36.229312+00:00"},{"alias_kind":"pith_short_12","alias_value":"EUVYO5DT2FSW","created_at":"2026-07-05T10:01:36.229312+00:00"},{"alias_kind":"pith_short_16","alias_value":"EUVYO5DT2FSW4TLK","created_at":"2026-07-05T10:01:36.229312+00:00"},{"alias_kind":"pith_short_8","alias_value":"EUVYO5DT","created_at":"2026-07-05T10:01:36.229312+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/EUVYO5DT2FSW4TLK6ZWPZCRS26","json":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26.json","graph_json":"https://pith.science/api/pith-number/EUVYO5DT2FSW4TLK6ZWPZCRS26/graph.json","events_json":"https://pith.science/api/pith-number/EUVYO5DT2FSW4TLK6ZWPZCRS26/events.json","paper":"https://pith.science/paper/EUVYO5DT"},"agent_actions":{"view_html":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26","download_json":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26.json","view_paper":"https://pith.science/paper/EUVYO5DT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.04874&json=true","fetch_graph":"https://pith.science/api/pith-number/EUVYO5DT2FSW4TLK6ZWPZCRS26/graph.json","fetch_events":"https://pith.science/api/pith-number/EUVYO5DT2FSW4TLK6ZWPZCRS26/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26/action/storage_attestation","attest_author":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26/action/author_attestation","sign_citation":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26/action/citation_signature","submit_replication":"https://pith.science/pith/EUVYO5DT2FSW4TLK6ZWPZCRS26/action/replication_record"}},"created_at":"2026-07-05T10:01:36.229312+00:00","updated_at":"2026-07-05T10:01:36.229312+00:00"}