{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5OKCUYHZMTT2EWT42D657XNBY3","short_pith_number":"pith:5OKCUYHZ","schema_version":"1.0","canonical_sha256":"eb942a60f964e7a25a7cd0fddfdda1c6ede6841cf2c28579f3740816f34d5ecd","source":{"kind":"arxiv","id":"2404.09126","version":2},"attestation_state":"computed","paper":{"title":"Treatment Effect Heterogeneity and Importance Measures for Multivariate Continuous Treatments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Antonio Linero, Danielle Braun, Heejun Shin, Joseph Antonelli, Kezia Irene, Michelle Audirac","submitted_at":"2024-04-14T02:35:23Z","abstract_excerpt":"Estimating the joint effect of a multivariate, continuous exposure is crucial, particularly in environmental health where interest lies in simultaneously evaluating the impact of multiple environmental pollutants on health. We develop novel methodology that addresses two key issues for estimation of treatment effects of multivariate, continuous exposures. We use nonparametric Bayesian methodology that is flexible to ensure our approach can capture a wide range of data generating processes. Additionally, we allow the effect of the exposures to be heterogeneous with respect to covariates. Treatm"},"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":"2404.09126","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-04-14T02:35:23Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"7e28132af1933a096bcaed90eb9271c93350d557d7235c8d5d85e362bfa13e09","abstract_canon_sha256":"616ffccc07019a49127c86b2ec5299becb67072fe99988845f84d87acad9f57d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:35.339241Z","signature_b64":"XcIyn5VbKfJtEMCwWtsa++54BqMpYCXevH9ccuHGaGiwrVX8tOSO1fTZ5nZ9Is0sHUG0/q8fPAkw6NMGrhd0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb942a60f964e7a25a7cd0fddfdda1c6ede6841cf2c28579f3740816f34d5ecd","last_reissued_at":"2026-07-05T09:56:35.338706Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:35.338706Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Treatment Effect Heterogeneity and Importance Measures for Multivariate Continuous Treatments","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Antonio Linero, Danielle Braun, Heejun Shin, Joseph Antonelli, Kezia Irene, Michelle Audirac","submitted_at":"2024-04-14T02:35:23Z","abstract_excerpt":"Estimating the joint effect of a multivariate, continuous exposure is crucial, particularly in environmental health where interest lies in simultaneously evaluating the impact of multiple environmental pollutants on health. We develop novel methodology that addresses two key issues for estimation of treatment effects of multivariate, continuous exposures. We use nonparametric Bayesian methodology that is flexible to ensure our approach can capture a wide range of data generating processes. Additionally, we allow the effect of the exposures to be heterogeneous with respect to covariates. Treatm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.09126","kind":"arxiv","version":2},"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/2404.09126/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":"2404.09126","created_at":"2026-07-05T09:56:35.338770+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.09126v2","created_at":"2026-07-05T09:56:35.338770+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.09126","created_at":"2026-07-05T09:56:35.338770+00:00"},{"alias_kind":"pith_short_12","alias_value":"5OKCUYHZMTT2","created_at":"2026-07-05T09:56:35.338770+00:00"},{"alias_kind":"pith_short_16","alias_value":"5OKCUYHZMTT2EWT4","created_at":"2026-07-05T09:56:35.338770+00:00"},{"alias_kind":"pith_short_8","alias_value":"5OKCUYHZ","created_at":"2026-07-05T09:56:35.338770+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":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3","json":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3.json","graph_json":"https://pith.science/api/pith-number/5OKCUYHZMTT2EWT42D657XNBY3/graph.json","events_json":"https://pith.science/api/pith-number/5OKCUYHZMTT2EWT42D657XNBY3/events.json","paper":"https://pith.science/paper/5OKCUYHZ"},"agent_actions":{"view_html":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3","download_json":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3.json","view_paper":"https://pith.science/paper/5OKCUYHZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.09126&json=true","fetch_graph":"https://pith.science/api/pith-number/5OKCUYHZMTT2EWT42D657XNBY3/graph.json","fetch_events":"https://pith.science/api/pith-number/5OKCUYHZMTT2EWT42D657XNBY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3/action/storage_attestation","attest_author":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3/action/author_attestation","sign_citation":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3/action/citation_signature","submit_replication":"https://pith.science/pith/5OKCUYHZMTT2EWT42D657XNBY3/action/replication_record"}},"created_at":"2026-07-05T09:56:35.338770+00:00","updated_at":"2026-07-05T09:56:35.338770+00:00"}