{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LEZSF4WACR2NBPEGTAAREI4MCS","short_pith_number":"pith:LEZSF4WA","schema_version":"1.0","canonical_sha256":"593322f2c01474d0bc86980112238c14b7e5c0565fce771f4509122c0ca07751","source":{"kind":"arxiv","id":"2303.01620","version":1},"attestation_state":"computed","paper":{"title":"Estimating Heterogeneous Causal Mediation Effects with Bayesian Decision Tree Ensembles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Angela Ting, Antonio R. Linero","submitted_at":"2023-03-02T22:52:45Z","abstract_excerpt":"The causal inference literature has increasingly recognized that explicitly targeting treatment effect heterogeneity can lead to improved scientific understanding and policy recommendations. Towards the same ends, studying the causal pathway connecting the treatment to the outcome can be also useful. This paper addresses these problems in the context of \\emph{causal mediation analysis}. We introduce a varying coefficient model based on Bayesian additive regression trees to identify and regularize heterogeneous causal mediation effects; analogously with linear structural equation models, these "},"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":"2303.01620","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2023-03-02T22:52:45Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"b527dfae0fbc9c5d2fc8455ef068ce20aae991a093872ea0efe7498e999994a2","abstract_canon_sha256":"4cf23e967861da09e793694cbc562255ae532c8afc6a7aca9d93b46b0997def8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:47:36.501988Z","signature_b64":"U7ZBYCZxekiOdGQ7AjxSxTPr+bIfa2ci6xwBXHPOhhEBRPTSAs3Gs1Rkc5uoyKabmGz/9hkT2Hbxus1KcYZkBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"593322f2c01474d0bc86980112238c14b7e5c0565fce771f4509122c0ca07751","last_reissued_at":"2026-07-05T05:47:36.501594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:47:36.501594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Heterogeneous Causal Mediation Effects with Bayesian Decision Tree Ensembles","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Angela Ting, Antonio R. Linero","submitted_at":"2023-03-02T22:52:45Z","abstract_excerpt":"The causal inference literature has increasingly recognized that explicitly targeting treatment effect heterogeneity can lead to improved scientific understanding and policy recommendations. Towards the same ends, studying the causal pathway connecting the treatment to the outcome can be also useful. This paper addresses these problems in the context of \\emph{causal mediation analysis}. We introduce a varying coefficient model based on Bayesian additive regression trees to identify and regularize heterogeneous causal mediation effects; analogously with linear structural equation models, these "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.01620","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/2303.01620/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":"2303.01620","created_at":"2026-07-05T05:47:36.501652+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.01620v1","created_at":"2026-07-05T05:47:36.501652+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.01620","created_at":"2026-07-05T05:47:36.501652+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEZSF4WACR2N","created_at":"2026-07-05T05:47:36.501652+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEZSF4WACR2NBPEG","created_at":"2026-07-05T05:47:36.501652+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEZSF4WA","created_at":"2026-07-05T05:47:36.501652+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":45,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS","json":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS.json","graph_json":"https://pith.science/api/pith-number/LEZSF4WACR2NBPEGTAAREI4MCS/graph.json","events_json":"https://pith.science/api/pith-number/LEZSF4WACR2NBPEGTAAREI4MCS/events.json","paper":"https://pith.science/paper/LEZSF4WA"},"agent_actions":{"view_html":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS","download_json":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS.json","view_paper":"https://pith.science/paper/LEZSF4WA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.01620&json=true","fetch_graph":"https://pith.science/api/pith-number/LEZSF4WACR2NBPEGTAAREI4MCS/graph.json","fetch_events":"https://pith.science/api/pith-number/LEZSF4WACR2NBPEGTAAREI4MCS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS/action/storage_attestation","attest_author":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS/action/author_attestation","sign_citation":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS/action/citation_signature","submit_replication":"https://pith.science/pith/LEZSF4WACR2NBPEGTAAREI4MCS/action/replication_record"}},"created_at":"2026-07-05T05:47:36.501652+00:00","updated_at":"2026-07-05T05:47:36.501652+00:00"}