{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:34OXRTZ4REXGYECJUMKPPRDDYV","short_pith_number":"pith:34OXRTZ4","schema_version":"1.0","canonical_sha256":"df1d78cf3c892e6c1049a314f7c463c55e6e22026d66f4dede674ab924273509","source":{"kind":"arxiv","id":"1812.07153","version":1},"attestation_state":"computed","paper":{"title":"Gaussian Process Mixtures for Estimating Heterogeneous Treatment Effects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.OT"],"primary_cat":"stat.ME","authors_text":"Abbas Zaidi, Sayan Mukherjee","submitted_at":"2018-12-18T03:43:41Z","abstract_excerpt":"We develop a Gaussian-process mixture model for heterogeneous treatment effect estimation that leverages the use of transformed outcomes. The approach we will present attempts to improve point estimation and uncertainty quantification relative to past work that has used transformed variable related methods as well as traditional outcome modeling. Earlier work on modeling treatment effect heterogeneity using transformed outcomes has relied on tree based methods such as single regression trees and random forests. Under the umbrella of non-parametric models, outcome modeling has been performed us"},"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":"1812.07153","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-12-18T03:43:41Z","cross_cats_sorted":["stat.OT"],"title_canon_sha256":"111e8fbe8a275fd8b57a6131238f5d968770f82ef37965ceec517f87cf6c5841","abstract_canon_sha256":"33ded3885be725b6bf5af66dc5026b5a85d81bba25658ecae81e4384626b4f54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:07.798041Z","signature_b64":"bhgIOg5fIebR9IqVz7YGfihsqE+VUr74A904z4eIZ7E9Yx/u+MpLlkn3BFgeJVCFTCh3oxjB4tpPezIVq6n6BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df1d78cf3c892e6c1049a314f7c463c55e6e22026d66f4dede674ab924273509","last_reissued_at":"2026-05-17T23:58:07.797544Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:07.797544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gaussian Process Mixtures for Estimating Heterogeneous Treatment Effects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.OT"],"primary_cat":"stat.ME","authors_text":"Abbas Zaidi, Sayan Mukherjee","submitted_at":"2018-12-18T03:43:41Z","abstract_excerpt":"We develop a Gaussian-process mixture model for heterogeneous treatment effect estimation that leverages the use of transformed outcomes. The approach we will present attempts to improve point estimation and uncertainty quantification relative to past work that has used transformed variable related methods as well as traditional outcome modeling. Earlier work on modeling treatment effect heterogeneity using transformed outcomes has relied on tree based methods such as single regression trees and random forests. Under the umbrella of non-parametric models, outcome modeling has been performed us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.07153","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":"1812.07153","created_at":"2026-05-17T23:58:07.797621+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.07153v1","created_at":"2026-05-17T23:58:07.797621+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.07153","created_at":"2026-05-17T23:58:07.797621+00:00"},{"alias_kind":"pith_short_12","alias_value":"34OXRTZ4REXG","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"34OXRTZ4REXGYECJ","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"34OXRTZ4","created_at":"2026-05-18T12:32:02.567920+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/34OXRTZ4REXGYECJUMKPPRDDYV","json":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV.json","graph_json":"https://pith.science/api/pith-number/34OXRTZ4REXGYECJUMKPPRDDYV/graph.json","events_json":"https://pith.science/api/pith-number/34OXRTZ4REXGYECJUMKPPRDDYV/events.json","paper":"https://pith.science/paper/34OXRTZ4"},"agent_actions":{"view_html":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV","download_json":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV.json","view_paper":"https://pith.science/paper/34OXRTZ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.07153&json=true","fetch_graph":"https://pith.science/api/pith-number/34OXRTZ4REXGYECJUMKPPRDDYV/graph.json","fetch_events":"https://pith.science/api/pith-number/34OXRTZ4REXGYECJUMKPPRDDYV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV/action/storage_attestation","attest_author":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV/action/author_attestation","sign_citation":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV/action/citation_signature","submit_replication":"https://pith.science/pith/34OXRTZ4REXGYECJUMKPPRDDYV/action/replication_record"}},"created_at":"2026-05-17T23:58:07.797621+00:00","updated_at":"2026-05-17T23:58:07.797621+00:00"}