{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BZZAVZOAWQFAM5EKZOB5YX2TFK","short_pith_number":"pith:BZZAVZOA","schema_version":"1.0","canonical_sha256":"0e720ae5c0b40a06748acb83dc5f532ab13ed45e2024374fde460a6062fedead","source":{"kind":"arxiv","id":"2306.12394","version":1},"attestation_state":"computed","paper":{"title":"Optimal allocation of sample size for randomization-based inference from $2^K$ factorial designs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Arun Ravichandran, Brian Libgober, Nicole E. Pashley, Tirthankar Dasgupta","submitted_at":"2023-06-21T17:28:20Z","abstract_excerpt":"Optimizing the allocation of units into treatment groups can help researchers improve the precision of causal estimators and decrease costs when running factorial experiments. However, existing optimal allocation results typically assume a super-population model and that the outcome data comes from a known family of distributions. Instead, we focus on randomization-based causal inference for the finite-population setting, which does not require model specifications for the data or sampling assumptions. We propose exact theoretical solutions for optimal allocation in $2^K$ factorial experiments"},"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":"2306.12394","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-06-21T17:28:20Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"2a0dba4814312feb1a1debeb0317ac811f02c0cfbddf5db8a54ea4022e94dec6","abstract_canon_sha256":"5d13e351ff93b1f87752cfbb91df5831aed982c1ed4de9cf03c483e70f87d040"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:56.151606Z","signature_b64":"8cpMaD1vE91xAXl/FMZZQFn4RkubsZu+IxZiNOWv1F31yya8/G650cZ33aNThRreOrzcc7Xul8oHdQ5NIuniCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e720ae5c0b40a06748acb83dc5f532ab13ed45e2024374fde460a6062fedead","last_reissued_at":"2026-07-05T08:24:56.151156Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:56.151156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal allocation of sample size for randomization-based inference from $2^K$ factorial designs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Arun Ravichandran, Brian Libgober, Nicole E. Pashley, Tirthankar Dasgupta","submitted_at":"2023-06-21T17:28:20Z","abstract_excerpt":"Optimizing the allocation of units into treatment groups can help researchers improve the precision of causal estimators and decrease costs when running factorial experiments. However, existing optimal allocation results typically assume a super-population model and that the outcome data comes from a known family of distributions. Instead, we focus on randomization-based causal inference for the finite-population setting, which does not require model specifications for the data or sampling assumptions. We propose exact theoretical solutions for optimal allocation in $2^K$ factorial experiments"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.12394","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/2306.12394/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":"2306.12394","created_at":"2026-07-05T08:24:56.151214+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.12394v1","created_at":"2026-07-05T08:24:56.151214+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.12394","created_at":"2026-07-05T08:24:56.151214+00:00"},{"alias_kind":"pith_short_12","alias_value":"BZZAVZOAWQFA","created_at":"2026-07-05T08:24:56.151214+00:00"},{"alias_kind":"pith_short_16","alias_value":"BZZAVZOAWQFAM5EK","created_at":"2026-07-05T08:24:56.151214+00:00"},{"alias_kind":"pith_short_8","alias_value":"BZZAVZOA","created_at":"2026-07-05T08:24:56.151214+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/BZZAVZOAWQFAM5EKZOB5YX2TFK","json":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK.json","graph_json":"https://pith.science/api/pith-number/BZZAVZOAWQFAM5EKZOB5YX2TFK/graph.json","events_json":"https://pith.science/api/pith-number/BZZAVZOAWQFAM5EKZOB5YX2TFK/events.json","paper":"https://pith.science/paper/BZZAVZOA"},"agent_actions":{"view_html":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK","download_json":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK.json","view_paper":"https://pith.science/paper/BZZAVZOA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.12394&json=true","fetch_graph":"https://pith.science/api/pith-number/BZZAVZOAWQFAM5EKZOB5YX2TFK/graph.json","fetch_events":"https://pith.science/api/pith-number/BZZAVZOAWQFAM5EKZOB5YX2TFK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK/action/storage_attestation","attest_author":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK/action/author_attestation","sign_citation":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK/action/citation_signature","submit_replication":"https://pith.science/pith/BZZAVZOAWQFAM5EKZOB5YX2TFK/action/replication_record"}},"created_at":"2026-07-05T08:24:56.151214+00:00","updated_at":"2026-07-05T08:24:56.151214+00:00"}