{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TDOY7CMMH2CZKNPVWCGCN4UIBJ","short_pith_number":"pith:TDOY7CMM","schema_version":"1.0","canonical_sha256":"98dd8f898c3e859535f5b08c26f2880a60ec7868581b3a64b7468bd09cb0c33e","source":{"kind":"arxiv","id":"2408.05342","version":4},"attestation_state":"computed","paper":{"title":"ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Chengchun Shi, Hongtu Zhu, Ke Sun, Linglong Kong","submitted_at":"2024-08-09T21:20:55Z","abstract_excerpt":"Online experiments %in which experimental units receive a sequence of treatments over time are frequently employed in many technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. In many applications, the experimental units receive a sequence of treatments over time. To handle these time-dependent settings, existing A/B testing solutions typically assume a fully observable experimental environment that satisfies the Markov condition. However, this assumption often does not hold in practice.\n  This paper studies 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":"2408.05342","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2024-08-09T21:20:55Z","cross_cats_sorted":[],"title_canon_sha256":"6f2d53e47d8d3e29c7a581a05c728fb39d2da1909b09edd210786a8fec3784ea","abstract_canon_sha256":"b1b5d0ec74828c42d9227f0b883a5864b40dda7401f5fbcc75a6ab95fa94fa40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:59:51.003016Z","signature_b64":"AByabx7v06KFVvvzNBS4OkCZVW3wQk8b6MjQEnbmqoGPk4Fwe+9jVDBlw8bmL+o4VHwA0n1zhH8/hoQliZhTCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98dd8f898c3e859535f5b08c26f2880a60ec7868581b3a64b7468bd09cb0c33e","last_reissued_at":"2026-07-05T09:59:51.002605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:59:51.002605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"Chengchun Shi, Hongtu Zhu, Ke Sun, Linglong Kong","submitted_at":"2024-08-09T21:20:55Z","abstract_excerpt":"Online experiments %in which experimental units receive a sequence of treatments over time are frequently employed in many technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. In many applications, the experimental units receive a sequence of treatments over time. To handle these time-dependent settings, existing A/B testing solutions typically assume a fully observable experimental environment that satisfies the Markov condition. However, this assumption often does not hold in practice.\n  This paper studies the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.05342","kind":"arxiv","version":4},"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/2408.05342/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":"2408.05342","created_at":"2026-07-05T09:59:51.002663+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.05342v4","created_at":"2026-07-05T09:59:51.002663+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.05342","created_at":"2026-07-05T09:59:51.002663+00:00"},{"alias_kind":"pith_short_12","alias_value":"TDOY7CMMH2CZ","created_at":"2026-07-05T09:59:51.002663+00:00"},{"alias_kind":"pith_short_16","alias_value":"TDOY7CMMH2CZKNPV","created_at":"2026-07-05T09:59:51.002663+00:00"},{"alias_kind":"pith_short_8","alias_value":"TDOY7CMM","created_at":"2026-07-05T09:59:51.002663+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.15108","citing_title":"Logging Policy Design for Off-Policy Evaluation","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15108","citing_title":"Logging Policy Design for Off-Policy Evaluation","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12899","citing_title":"Robust Sequential Experimental Design for A/B Testing","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21849","citing_title":"Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ","json":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ.json","graph_json":"https://pith.science/api/pith-number/TDOY7CMMH2CZKNPVWCGCN4UIBJ/graph.json","events_json":"https://pith.science/api/pith-number/TDOY7CMMH2CZKNPVWCGCN4UIBJ/events.json","paper":"https://pith.science/paper/TDOY7CMM"},"agent_actions":{"view_html":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ","download_json":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ.json","view_paper":"https://pith.science/paper/TDOY7CMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.05342&json=true","fetch_graph":"https://pith.science/api/pith-number/TDOY7CMMH2CZKNPVWCGCN4UIBJ/graph.json","fetch_events":"https://pith.science/api/pith-number/TDOY7CMMH2CZKNPVWCGCN4UIBJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ/action/storage_attestation","attest_author":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ/action/author_attestation","sign_citation":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ/action/citation_signature","submit_replication":"https://pith.science/pith/TDOY7CMMH2CZKNPVWCGCN4UIBJ/action/replication_record"}},"created_at":"2026-07-05T09:59:51.002663+00:00","updated_at":"2026-07-05T09:59:51.002663+00:00"}