{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:LYFT24IMHCXUVQQJGNA5HDCGBM","short_pith_number":"pith:LYFT24IM","schema_version":"1.0","canonical_sha256":"5e0b3d710c38af4ac2093341d38c460b2d144018c6c6d70c9aa0666bed179501","source":{"kind":"arxiv","id":"2508.12688","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Double Machine Learning for Causal Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Francis J. DiTraglia, Laura Liu","submitted_at":"2025-08-18T07:41:06Z","abstract_excerpt":"This paper proposes a simple, novel, and fully-Bayesian approach for causal inference in partially linear models with high-dimensional control variables. Off-the-shelf machine learning methods can introduce biases in the causal parameter known as regularization-induced confounding. To address this, we propose a Bayesian Double Machine Learning (BDML) method, which modifies a standard Bayesian multivariate regression model and recovers the causal effect of interest from the reduced-form covariance matrix. Our BDML is related to the burgeoning frequentist literature on DML while addressing its l"},"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":"2508.12688","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2025-08-18T07:41:06Z","cross_cats_sorted":[],"title_canon_sha256":"0af4d424f295acf6ee27e22712300984a8dec89a88ccf4690ca9ccce5bae8277","abstract_canon_sha256":"56edd1048912069c59c836e578c54b78157f05217ac1f6374420c9b8cba2f17d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:55:25.398407Z","signature_b64":"o7igI2//Q3z4LZcWMysEdpwD8khVNt8L++LNj8v5aEBg6MY2Xq7kUDixB0xlDHDc+VWIuvof24JmvAHFW49mBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e0b3d710c38af4ac2093341d38c460b2d144018c6c6d70c9aa0666bed179501","last_reissued_at":"2026-07-05T11:55:25.397948Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:55:25.397948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Double Machine Learning for Causal Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Francis J. DiTraglia, Laura Liu","submitted_at":"2025-08-18T07:41:06Z","abstract_excerpt":"This paper proposes a simple, novel, and fully-Bayesian approach for causal inference in partially linear models with high-dimensional control variables. Off-the-shelf machine learning methods can introduce biases in the causal parameter known as regularization-induced confounding. To address this, we propose a Bayesian Double Machine Learning (BDML) method, which modifies a standard Bayesian multivariate regression model and recovers the causal effect of interest from the reduced-form covariance matrix. Our BDML is related to the burgeoning frequentist literature on DML while addressing its l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.12688","kind":"arxiv","version":1},"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/2508.12688/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":"2508.12688","created_at":"2026-07-05T11:55:25.398005+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.12688v1","created_at":"2026-07-05T11:55:25.398005+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.12688","created_at":"2026-07-05T11:55:25.398005+00:00"},{"alias_kind":"pith_short_12","alias_value":"LYFT24IMHCXU","created_at":"2026-07-05T11:55:25.398005+00:00"},{"alias_kind":"pith_short_16","alias_value":"LYFT24IMHCXUVQQJ","created_at":"2026-07-05T11:55:25.398005+00:00"},{"alias_kind":"pith_short_8","alias_value":"LYFT24IM","created_at":"2026-07-05T11:55:25.398005+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.05832","citing_title":"Decision Theoretic Subgroup Detection With Bayesian Machine Learning","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM","json":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM.json","graph_json":"https://pith.science/api/pith-number/LYFT24IMHCXUVQQJGNA5HDCGBM/graph.json","events_json":"https://pith.science/api/pith-number/LYFT24IMHCXUVQQJGNA5HDCGBM/events.json","paper":"https://pith.science/paper/LYFT24IM"},"agent_actions":{"view_html":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM","download_json":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM.json","view_paper":"https://pith.science/paper/LYFT24IM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.12688&json=true","fetch_graph":"https://pith.science/api/pith-number/LYFT24IMHCXUVQQJGNA5HDCGBM/graph.json","fetch_events":"https://pith.science/api/pith-number/LYFT24IMHCXUVQQJGNA5HDCGBM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM/action/storage_attestation","attest_author":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM/action/author_attestation","sign_citation":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM/action/citation_signature","submit_replication":"https://pith.science/pith/LYFT24IMHCXUVQQJGNA5HDCGBM/action/replication_record"}},"created_at":"2026-07-05T11:55:25.398005+00:00","updated_at":"2026-07-05T11:55:25.398005+00:00"}