{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UXT3PDYYZPEVTRV5K4NQVBM3HJ","short_pith_number":"pith:UXT3PDYY","schema_version":"1.0","canonical_sha256":"a5e7b78f18cbc959c6bd571b0a859b3a6616713b997df99fac29703a61fec974","source":{"kind":"arxiv","id":"2109.06940","version":1},"attestation_state":"computed","paper":{"title":"Choosing an Optimal Method for Causal Decomposition Analysis: A Better Practice for Identifying Contributing Factors to Health Disparities","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Chioun Lee, Soojin Park, Suyeon Kang","submitted_at":"2021-09-14T19:34:59Z","abstract_excerpt":"Causal decomposition analysis provides a way to identify mediators that contribute to health disparities between marginalized and non-marginalized groups. In particular, the degree to which a disparity would be reduced or remain after intervening on a mediator is of interest. Yet, estimating disparity reduction and remaining might be challenging for many researchers, possibly because there is a lack of understanding of how each estimation method differs from other methods. In addition, there is no appropriate estimation method available for a certain setting (i.e., a regression-based approach "},"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":"2109.06940","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"stat.ME","submitted_at":"2021-09-14T19:34:59Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"2faf41b6bfab16c52dfcda651e851c30b27876c1cfa2879a174bbb53cbf25b64","abstract_canon_sha256":"c9ff0fa80105470c1e1af191c35337f234486acae3e13e36c0ba56fc04896552"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:14:37.391582Z","signature_b64":"TEetntOIpNLpnfP28QP7GI5Iy2qQSXAw9ionRkN9+Qu2zlMTAmUq80Id9m5zmSkWNPtadCzxOu83B1stiPpIBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5e7b78f18cbc959c6bd571b0a859b3a6616713b997df99fac29703a61fec974","last_reissued_at":"2026-07-05T03:14:37.391185Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:14:37.391185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Choosing an Optimal Method for Causal Decomposition Analysis: A Better Practice for Identifying Contributing Factors to Health Disparities","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Chioun Lee, Soojin Park, Suyeon Kang","submitted_at":"2021-09-14T19:34:59Z","abstract_excerpt":"Causal decomposition analysis provides a way to identify mediators that contribute to health disparities between marginalized and non-marginalized groups. In particular, the degree to which a disparity would be reduced or remain after intervening on a mediator is of interest. Yet, estimating disparity reduction and remaining might be challenging for many researchers, possibly because there is a lack of understanding of how each estimation method differs from other methods. In addition, there is no appropriate estimation method available for a certain setting (i.e., a regression-based approach "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.06940","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/2109.06940/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":"2109.06940","created_at":"2026-07-05T03:14:37.391242+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.06940v1","created_at":"2026-07-05T03:14:37.391242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.06940","created_at":"2026-07-05T03:14:37.391242+00:00"},{"alias_kind":"pith_short_12","alias_value":"UXT3PDYYZPEV","created_at":"2026-07-05T03:14:37.391242+00:00"},{"alias_kind":"pith_short_16","alias_value":"UXT3PDYYZPEVTRV5","created_at":"2026-07-05T03:14:37.391242+00:00"},{"alias_kind":"pith_short_8","alias_value":"UXT3PDYY","created_at":"2026-07-05T03:14:37.391242+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.16975","citing_title":"Causal Variance Decompositions for Measuring Health Inequalities","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ","json":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ.json","graph_json":"https://pith.science/api/pith-number/UXT3PDYYZPEVTRV5K4NQVBM3HJ/graph.json","events_json":"https://pith.science/api/pith-number/UXT3PDYYZPEVTRV5K4NQVBM3HJ/events.json","paper":"https://pith.science/paper/UXT3PDYY"},"agent_actions":{"view_html":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ","download_json":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ.json","view_paper":"https://pith.science/paper/UXT3PDYY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.06940&json=true","fetch_graph":"https://pith.science/api/pith-number/UXT3PDYYZPEVTRV5K4NQVBM3HJ/graph.json","fetch_events":"https://pith.science/api/pith-number/UXT3PDYYZPEVTRV5K4NQVBM3HJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ/action/storage_attestation","attest_author":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ/action/author_attestation","sign_citation":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ/action/citation_signature","submit_replication":"https://pith.science/pith/UXT3PDYYZPEVTRV5K4NQVBM3HJ/action/replication_record"}},"created_at":"2026-07-05T03:14:37.391242+00:00","updated_at":"2026-07-05T03:14:37.391242+00:00"}