{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TYXDASDES2F2MVKFQAGHEUVEAT","short_pith_number":"pith:TYXDASDE","schema_version":"1.0","canonical_sha256":"9e2e304864968ba65545800c7252a404dce3747505e3f6e97b3ccf6cf77022eb","source":{"kind":"arxiv","id":"2404.02141","version":5},"attestation_state":"computed","paper":{"title":"Robustly estimating heterogeneity in factorial data using Rashomon Partitions","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","econ.EM","stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Anirudh Sankar, Aparajithan Venkateswaran, Arun G. Chandrasekhar, Tyler H. McCormick","submitted_at":"2024-04-02T17:53:28Z","abstract_excerpt":"In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates. Choosing a model that is too simple can obfuscate important heterogeneity in outcomes between covariate groups, while too much complexity risks identifying spurious patterns. In this paper, we propose a novel Bayesian framework for model uncertainty called Rashomon Partition Sets (RPSs). The RPS consists of all models that have posterior density close to the maximum a posteriori (MAP) model. We construct the"},"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":"2404.02141","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2024-04-02T17:53:28Z","cross_cats_sorted":["cs.LG","econ.EM","stat.CO","stat.ML"],"title_canon_sha256":"f44060333d1cf6644c502628e3c260a9841828532ad526524d2f470e2186dc01","abstract_canon_sha256":"34a6917d11dd5b2a051a6b1fc3118c82a2eb11fc37dc35323b294681780bcaa5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:59.171298Z","signature_b64":"ON9EVWkPjfYu5JdzeBFkWBJSIQfT3yXfukEBG6RZTRDUTu2tcvpXWl+o55te8cpJNzg/617nzWiwHsqutlO4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9e2e304864968ba65545800c7252a404dce3747505e3f6e97b3ccf6cf77022eb","last_reissued_at":"2026-06-08T01:04:59.170247Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:59.170247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robustly estimating heterogeneity in factorial data using Rashomon Partitions","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","econ.EM","stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Anirudh Sankar, Aparajithan Venkateswaran, Arun G. Chandrasekhar, Tyler H. McCormick","submitted_at":"2024-04-02T17:53:28Z","abstract_excerpt":"In both observational data and randomized control trials, researchers select statistical models to articulate how the outcome of interest varies with combinations of observable covariates. Choosing a model that is too simple can obfuscate important heterogeneity in outcomes between covariate groups, while too much complexity risks identifying spurious patterns. In this paper, we propose a novel Bayesian framework for model uncertainty called Rashomon Partition Sets (RPSs). The RPS consists of all models that have posterior density close to the maximum a posteriori (MAP) model. We construct the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.02141","kind":"arxiv","version":5},"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/2404.02141/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":"2404.02141","created_at":"2026-06-08T01:04:59.170393+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.02141v5","created_at":"2026-06-08T01:04:59.170393+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.02141","created_at":"2026-06-08T01:04:59.170393+00:00"},{"alias_kind":"pith_short_12","alias_value":"TYXDASDES2F2","created_at":"2026-06-08T01:04:59.170393+00:00"},{"alias_kind":"pith_short_16","alias_value":"TYXDASDES2F2MVKF","created_at":"2026-06-08T01:04:59.170393+00:00"},{"alias_kind":"pith_short_8","alias_value":"TYXDASDE","created_at":"2026-06-08T01:04:59.170393+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/TYXDASDES2F2MVKFQAGHEUVEAT","json":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT.json","graph_json":"https://pith.science/api/pith-number/TYXDASDES2F2MVKFQAGHEUVEAT/graph.json","events_json":"https://pith.science/api/pith-number/TYXDASDES2F2MVKFQAGHEUVEAT/events.json","paper":"https://pith.science/paper/TYXDASDE"},"agent_actions":{"view_html":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT","download_json":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT.json","view_paper":"https://pith.science/paper/TYXDASDE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.02141&json=true","fetch_graph":"https://pith.science/api/pith-number/TYXDASDES2F2MVKFQAGHEUVEAT/graph.json","fetch_events":"https://pith.science/api/pith-number/TYXDASDES2F2MVKFQAGHEUVEAT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT/action/storage_attestation","attest_author":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT/action/author_attestation","sign_citation":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT/action/citation_signature","submit_replication":"https://pith.science/pith/TYXDASDES2F2MVKFQAGHEUVEAT/action/replication_record"}},"created_at":"2026-06-08T01:04:59.170393+00:00","updated_at":"2026-06-08T01:04:59.170393+00:00"}