{"id":"https://openalex.org/W7220915420","doi":"https://doi.org/10.48550/arxiv.2610.07831","title":"Design-Driven Inference for Online Experiments: Jointly Optimal Evidence Collection, Testing, and Estimation","display_name":"Design-Driven Inference for Online Experiments: Jointly Optimal Evidence Collection, Testing, and Estimation","publication_year":2026,"publication_date":"2026-10-06","ids":{"openalex":"https://openalex.org/W7220915420","doi":"https://doi.org/10.48550/arxiv.2610.07831"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.07831","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.07831","pdf_url":"https://arxiv.org/pdf/2610.07831","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.07831","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5157931487","display_name":"Jun Yu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu, Jun","raw_affiliation_strings":["School of Mathematics and Statistics , Beijing Institute of Technology , Beijing 100081 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics , Beijing Institute of Technology , Beijing 100081 , China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157985878","display_name":"Wenbiao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenbiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157988553","display_name":"Zhao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5157947255","display_name":"Lixing","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lixing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5157944766","display_name":"Zhu","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.8471999764442444,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.8471999764442444,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.07609999924898148,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.019899999722838402,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/online-experiments","display_name":"online experiments","score":0.7170000076293945},{"id":"https://openalex.org/keywords/early-stopping","display_name":"early stopping","score":0.847000002861023},{"id":"https://openalex.org/keywords/e-values","display_name":"E-value","score":0.6930000185966492},{"id":"https://openalex.org/keywords/e-processes","display_name":"e-processes","score":0.460999995470047},{"id":"https://openalex.org/keywords/treatment-effect-estimation","display_name":"treatment effect estimation","score":0.6579999923706055},{"id":"https://openalex.org/keywords/minimax-rate-optimality","display_name":"minimax rate optimality","score":0.746999979019165},{"id":"https://openalex.org/keywords/continuous-monitoring","display_name":"continuous monitoring","score":0.5789999961853027},{"id":"https://openalex.org/keywords/treatment-assignment","display_name":"treatment allocation","score":0.4740000069141388},{"id":"https://openalex.org/keywords/multifactorial-experiments","display_name":"multifactorial experiments","score":0.5320000052452087},{"id":"https://openalex.org/keywords/maximum-likelihood-estimation","display_name":"maximum likelihood estimation","score":0.5370000004768372}],"concepts":[{"id":"https://openalex.org/C149728462","wikidata":"https://www.wikidata.org/wiki/Q751319","display_name":"Minimax","level":2,"score":0.741100013256073},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5347999930381775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4560000002384186},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4542999863624573},{"id":"https://openalex.org/C194531419","wikidata":"https://www.wikidata.org/wiki/Q17104825","display_name":"Nuisance parameter","level":3,"score":0.43880000710487366},{"id":"https://openalex.org/C99888217","wikidata":"https://www.wikidata.org/wiki/Q1288707","display_name":"Stopping time","level":2,"score":0.41600000858306885},{"id":"https://openalex.org/C86426650","wikidata":"https://www.wikidata.org/wiki/Q7452504","display_name":"Sequential estimation","level":2,"score":0.4117000102996826},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3991999924182892},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C5465570","wikidata":"https://www.wikidata.org/wiki/Q5326898","display_name":"Early stopping","level":3,"score":0.34779998660087585},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C2983961125","wikidata":"https://www.wikidata.org/wiki/Q1288707","display_name":"Stopping rule","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C181789720","wikidata":"https://www.wikidata.org/wiki/Q4812191","display_name":"Asymptotically optimal algorithm","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2802000045776367},{"id":"https://openalex.org/C99414536","wikidata":"https://www.wikidata.org/wiki/Q7098950","display_name":"Optimal stopping","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.2547000050544739}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.07831","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.07831","pdf_url":"https://arxiv.org/pdf/2610.07831","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.07831","doi":"https://doi.org/10.48550/arxiv.2610.07831","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.07831","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.07831","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.07831","pdf_url":"https://arxiv.org/pdf/2610.07831","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7220915420.pdf","grobid_xml":"https://content.openalex.org/works/W7220915420.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W2115070480"],"related_works":["https://openalex.org/W4388482341","https://openalex.org/W4414972116","https://openalex.org/W7096567410","https://openalex.org/W3205328973","https://openalex.org/W2187424074","https://openalex.org/W4405103310","https://openalex.org/W2977858920","https://openalex.org/W6960136339","https://openalex.org/W7172337100","https://openalex.org/W3140838481"],"abstract_inverted_index":{"Online":[0],"multifactorial":[1],"experiments":[2,53],"requires":[3],"early":[4],"stopping":[5,13,169],"for":[6,78,101,111,128,142,153],"practically":[7],"negligible":[8],"effects":[9,57],"while":[10],"controlling":[11],"false":[12],"under":[14],"continuous":[15],"monitoring":[16],"and":[17,30,40,42,58,73,90,122,124,130,148,159,171],"retaining":[18],"precise":[19],"treatment-effect":[20],"estimation.":[21,131],"We":[22],"develop":[23],"a":[24,75,83,160],"design-driven":[25],"framework":[26],"that":[27,63],"chooses":[28],"allocation":[29,36,89,107],"the":[31,48,70,97,154],"evidence":[32,38,80,146],"rule":[33],"jointly,":[34],"treating":[35],"as":[37,44],"collection":[39],"testing":[41,129],"estimation":[43,114,151,172],"complementary":[45],"uses":[46],"of":[47,115],"same":[49,106],"evidence.":[50],"For":[51],"multifactor":[52],"with":[54],"nuisance":[55,67],"block":[56],"treatment-by-block":[59],"interactions,":[60],"we":[61],"show":[62],"block-orthogonal":[64],"designs":[65],"remove":[66],"contamination":[68],"from":[69],"treatment":[71,116],"score":[72],"form":[74],"complete":[76,156],"class":[77],"worst-case":[79,145],"growth.":[81],"Under":[82],"fixed":[84],"information":[85],"budget,":[86],"an":[87,91,135],"isotropic":[88],"explicit":[92],"radial":[93],"e-value":[94],"jointly":[95],"attain":[96],"minimax":[98,139],"rate":[99,140],"optimal":[100,110],"directionally":[102],"unknown":[103],"alternatives.":[104],"The":[105],"is":[108],"universally":[109],"maximum":[112],"likelihood":[113],"effects,":[117],"simultaneously":[118],"achieving":[119],"A-,":[120],"D-,":[121],"E-optimality":[123],"establishing":[125],"double":[126],"optimality":[127,141,152],"Batchwise":[132],"replication":[133],"yields":[134],"anytime-valid":[136],"e-process,":[137],"retains":[138],"cumulative":[143],"conditional":[144],"growth,":[147],"preserves":[149],"universal":[150],"prespecified":[155],"experiment.":[157],"Simulations":[158],"large":[161],"language":[162],"model":[163],"prompt":[164],"experiment":[165],"illustrate":[166],"gains":[167],"in":[168],"efficiency":[170],"precision.":[173]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-09T00:00:00"}
