{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GK4ML5QIARF6FC5XCIX5TLJWPI","short_pith_number":"pith:GK4ML5QI","schema_version":"1.0","canonical_sha256":"32b8c5f608044be28bb7122fd9ad367a2355375519520103764df88337c2cd52","source":{"kind":"arxiv","id":"1602.03972","version":1},"attestation_state":"computed","paper":{"title":"On Randomization-based and Regression-based Inferences for 2^K Factorial Designs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jiannan Lu","submitted_at":"2016-02-12T07:44:20Z","abstract_excerpt":"We extend the randomization-based causal inference framework in Dasgupta et al. (2015) for general 2^K factorial designs, and demonstrate the equivalence between regression-based and randomization-based inferences. Consequently, we justify the use of regression-based methods in 2^K factorial designs from a finite-population perspective."},"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":"1602.03972","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-02-12T07:44:20Z","cross_cats_sorted":[],"title_canon_sha256":"dc7b5dab81584f6ccfc07245f86476fa8e93e4fd9f933373f6c74e569181fe34","abstract_canon_sha256":"85b76153864a00fc13df1d017b793795f41bdeb7a6e0c345bedde1baac2a2a85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:55.019196Z","signature_b64":"fiGoBqhEn3h7kU4Sa1yyGNQWoevJMgrOhRKKf98o7Y4gWJ0uPRZiG7tDpvwkxAJTzCPlfqP8KwLajw9db2a0Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32b8c5f608044be28bb7122fd9ad367a2355375519520103764df88337c2cd52","last_reissued_at":"2026-05-18T01:20:55.018419Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:55.018419Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Randomization-based and Regression-based Inferences for 2^K Factorial Designs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jiannan Lu","submitted_at":"2016-02-12T07:44:20Z","abstract_excerpt":"We extend the randomization-based causal inference framework in Dasgupta et al. (2015) for general 2^K factorial designs, and demonstrate the equivalence between regression-based and randomization-based inferences. Consequently, we justify the use of regression-based methods in 2^K factorial designs from a finite-population perspective."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.03972","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":"1602.03972","created_at":"2026-05-18T01:20:55.018556+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.03972v1","created_at":"2026-05-18T01:20:55.018556+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.03972","created_at":"2026-05-18T01:20:55.018556+00:00"},{"alias_kind":"pith_short_12","alias_value":"GK4ML5QIARF6","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GK4ML5QIARF6FC5X","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GK4ML5QI","created_at":"2026-05-18T12:30:19.053100+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/GK4ML5QIARF6FC5XCIX5TLJWPI","json":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI.json","graph_json":"https://pith.science/api/pith-number/GK4ML5QIARF6FC5XCIX5TLJWPI/graph.json","events_json":"https://pith.science/api/pith-number/GK4ML5QIARF6FC5XCIX5TLJWPI/events.json","paper":"https://pith.science/paper/GK4ML5QI"},"agent_actions":{"view_html":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI","download_json":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI.json","view_paper":"https://pith.science/paper/GK4ML5QI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.03972&json=true","fetch_graph":"https://pith.science/api/pith-number/GK4ML5QIARF6FC5XCIX5TLJWPI/graph.json","fetch_events":"https://pith.science/api/pith-number/GK4ML5QIARF6FC5XCIX5TLJWPI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI/action/storage_attestation","attest_author":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI/action/author_attestation","sign_citation":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI/action/citation_signature","submit_replication":"https://pith.science/pith/GK4ML5QIARF6FC5XCIX5TLJWPI/action/replication_record"}},"created_at":"2026-05-18T01:20:55.018556+00:00","updated_at":"2026-05-18T01:20:55.018556+00:00"}