{"id":"https://openalex.org/W7154072971","doi":"https://doi.org/10.48550/arxiv.2604.08804","title":"Policy-Aware Design of Large-Scale Factorial Experiments","display_name":"Policy-Aware Design of Large-Scale Factorial Experiments","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7154072971","doi":"https://doi.org/10.48550/arxiv.2604.08804"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08804","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08804","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,"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.08804","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012702826","display_name":"Xin Wen","orcid":"https://orcid.org/0000-0001-8379-4149"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100330062","display_name":"Xi Chen","orcid":"https://orcid.org/0000-0003-4612-4044"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060973953","display_name":"Will Wei Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Will Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124313985","display_name":"Yichen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.5347999930381775,"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"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.5347999930381775,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.1356000006198883,"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.06920000165700912,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fractional-factorial-design","display_name":"Fractional factorial design","score":0.6798999905586243},{"id":"https://openalex.org/keywords/factorial","display_name":"Factorial","score":0.6083999872207642},{"id":"https://openalex.org/keywords/factorial-experiment","display_name":"Factorial experiment","score":0.6033999919891357},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.49779999256134033},{"id":"https://openalex.org/keywords/design-of-experiments","display_name":"Design of experiments","score":0.49160000681877136},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.46889999508857727},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4544999897480011}],"concepts":[{"id":"https://openalex.org/C16469947","wikidata":"https://www.wikidata.org/wiki/Q2400745","display_name":"Fractional factorial design","level":3,"score":0.6798999905586243},{"id":"https://openalex.org/C183763347","wikidata":"https://www.wikidata.org/wiki/Q120976","display_name":"Factorial","level":2,"score":0.6083999872207642},{"id":"https://openalex.org/C169222746","wikidata":"https://www.wikidata.org/wiki/Q4116558","display_name":"Factorial experiment","level":2,"score":0.6033999919891357},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5101000070571899},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.49779999256134033},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.49160000681877136},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4772000014781952},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4544999897480011},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38350000977516174},{"id":"https://openalex.org/C51255310","wikidata":"https://www.wikidata.org/wiki/Q1163016","display_name":"Tensor product","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3142000138759613},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.2957000136375427},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C120823896","wikidata":"https://www.wikidata.org/wiki/Q1043226","display_name":"Product design","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08804","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08804","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,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.08804","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08804","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,"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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Digital":[0],"firms":[1],"routinely":[2],"run":[3],"many":[4],"online":[5],"experiments":[6,39,60,90],"on":[7,122,193],"shared":[8],"user":[9],"populations.":[10],"When":[11],"product":[12,231],"decisions":[13],"are":[14,46],"compositional,":[15],"such":[16],"as":[17,100],"combinations":[18,145],"of":[19,28,113,169,171],"interface":[20],"elements,":[21],"flows,":[22],"messages,":[23],"or":[24],"incentives,":[25],"the":[26,62,105,108,135,143,162,167,172,176,184,203],"number":[27],"feasible":[29,234],"interventions":[30],"grows":[31],"combinatorially,":[32],"while":[33],"available":[34],"traffic":[35],"remains":[36],"limited.":[37],"Overlapping":[38],"can":[40,228],"therefore":[41],"generate":[42],"interaction":[43],"effects":[44],"that":[45,87,161],"poorly":[47],"handled":[48],"by":[49],"decentralized":[50],"A/B":[51],"testing.":[52],"We":[53,82,151],"study":[54],"how":[55,224],"to":[56,66,72,119,142,146],"design":[57,86,232],"large-scale":[58],"factorial":[59,94,186],"when":[61],"objective":[63],"is":[64],"not":[65],"estimate":[67],"every":[68],"treatment":[69],"effect,":[70],"but":[71],"identify":[73],"a":[74,78,84,92,101,111,148,194],"high-performing":[75],"policy":[76],"under":[77],"fixed":[79],"experimentation":[80,227],"budget.":[81],"propose":[83],"two-stage":[85],"centralizes":[88],"overlapping":[89],"into":[91],"single":[93],"problem":[95,196],"and":[96,125,156,175,211,218],"models":[97],"expected":[98],"outcomes":[99],"low-rank":[102,173],"tensor.":[103],"In":[104,134,188],"first":[106],"stage,":[107,137],"platform":[109,236],"samples":[110],"subset":[112],"intervention":[114],"combinations,":[115,124],"uses":[116],"tensor":[117,174,209],"completion":[118,210],"infer":[120],"performance":[121],"untested":[123],"eliminates":[126],"weak":[127],"factor":[128,180],"levels":[129],"using":[130],"estimated":[131],"marginal":[132],"contributions.":[133],"second":[136],"it":[138],"applies":[139],"sequential":[140],"halving":[141],"surviving":[144],"select":[147],"final":[149],"policy.":[150],"establish":[152],"gap-independent":[153],"simple-regret":[154],"bounds":[155],"gap-dependent":[157],"identification":[158],"guarantees":[159],"showing":[160],"relevant":[163],"complexity":[164],"scales":[165],"with":[166],"degrees":[168],"freedom":[170],"separation":[177],"structure":[178],"across":[179],"levels,":[181],"rather":[182],"than":[183],"full":[185],"size.":[187],"an":[189],"offline":[190],"evaluation":[191],"based":[192],"product-bundling":[195],"constructed":[197],"from":[198],"100":[199],"million":[200],"Taobao":[201],"interactions,":[202],"proposed":[204],"method":[205],"substantially":[206],"outperforms":[207],"one-shot":[208],"unstructured":[212],"best-arm":[213],"benchmarks,":[214],"especially":[215],"in":[216],"low-budget":[217],"high-noise":[219],"settings.":[220],"These":[221],"results":[222],"show":[223],"centralized,":[225],"policy-aware":[226],"make":[229],"combinatorial":[230],"operationally":[233],"at":[235],"scale.":[237]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-14T00:00:00"}