{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:MSSMCXZ2L5UQDYA2Y5L2VAEKTP","short_pith_number":"pith:MSSMCXZ2","schema_version":"1.0","canonical_sha256":"64a4c15f3a5f6901e01ac757aa808a9bc917a32a7921a0879c26ea61f26c8525","source":{"kind":"arxiv","id":"1808.09507","version":1},"attestation_state":"computed","paper":{"title":"Tree-Based Bayesian Treatment Effect Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Hedibert Freitas Lopes, Pedro Henrique Filipini dos Santos","submitted_at":"2018-08-28T19:31:28Z","abstract_excerpt":"The inclusion of the propensity score as a covariate in Bayesian regression trees for causal inference can reduce the bias in treatment effect estimations, which occurs due to the regularization-induced confounding phenomenon. This study advocate for the use of the propensity score by evaluating it under a full-Bayesian variable selection setting, and the use of Individual Conditional Expectation Plots, which is a graphical tool that can improve treatment effect analysis on tree-based Bayesian models and others \"black box\" models. The first one, even if poorly estimated, can lead to bias reduc"},"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":"1808.09507","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-28T19:31:28Z","cross_cats_sorted":[],"title_canon_sha256":"3bdb15d1a07dd3fa19ae1b74023431916381b3d27679f3179c39bbac591126db","abstract_canon_sha256":"f73af27eed5072309f296cd651890b6f5dd0599607183ebeeaf69f3c45ce5c98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:55.346597Z","signature_b64":"VuwKSq6YqB+21QP0R+hNOCne8fZQ9B+kS8OzYtLLnpgEqBJcaQxqgeRzN4AoFjtXae3zcUi5GZf0LtuH05DACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64a4c15f3a5f6901e01ac757aa808a9bc917a32a7921a0879c26ea61f26c8525","last_reissued_at":"2026-05-18T00:06:55.345915Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:55.345915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tree-Based Bayesian Treatment Effect Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Hedibert Freitas Lopes, Pedro Henrique Filipini dos Santos","submitted_at":"2018-08-28T19:31:28Z","abstract_excerpt":"The inclusion of the propensity score as a covariate in Bayesian regression trees for causal inference can reduce the bias in treatment effect estimations, which occurs due to the regularization-induced confounding phenomenon. This study advocate for the use of the propensity score by evaluating it under a full-Bayesian variable selection setting, and the use of Individual Conditional Expectation Plots, which is a graphical tool that can improve treatment effect analysis on tree-based Bayesian models and others \"black box\" models. The first one, even if poorly estimated, can lead to bias reduc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.09507","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":""},"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":"1808.09507","created_at":"2026-05-18T00:06:55.346020+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.09507v1","created_at":"2026-05-18T00:06:55.346020+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.09507","created_at":"2026-05-18T00:06:55.346020+00:00"},{"alias_kind":"pith_short_12","alias_value":"MSSMCXZ2L5UQ","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"MSSMCXZ2L5UQDYA2","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"MSSMCXZ2","created_at":"2026-05-18T12:32:40.477152+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/MSSMCXZ2L5UQDYA2Y5L2VAEKTP","json":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP.json","graph_json":"https://pith.science/api/pith-number/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/graph.json","events_json":"https://pith.science/api/pith-number/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/events.json","paper":"https://pith.science/paper/MSSMCXZ2"},"agent_actions":{"view_html":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP","download_json":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP.json","view_paper":"https://pith.science/paper/MSSMCXZ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.09507&json=true","fetch_graph":"https://pith.science/api/pith-number/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/graph.json","fetch_events":"https://pith.science/api/pith-number/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/action/storage_attestation","attest_author":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/action/author_attestation","sign_citation":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/action/citation_signature","submit_replication":"https://pith.science/pith/MSSMCXZ2L5UQDYA2Y5L2VAEKTP/action/replication_record"}},"created_at":"2026-05-18T00:06:55.346020+00:00","updated_at":"2026-05-18T00:06:55.346020+00:00"}