{"id":"https://openalex.org/W7214967334","doi":"https://doi.org/10.48550/arxiv.2609.37873","title":"Target-Aware Sequential Inference: Pooled versus Stratified Anytime-Valid Designs","display_name":"Target-Aware Sequential Inference: Pooled versus Stratified Anytime-Valid Designs","publication_year":2026,"publication_date":"2026-09-29","ids":{"openalex":"https://openalex.org/W7214967334","doi":"https://doi.org/10.48550/arxiv.2609.37873"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.37873","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37873","pdf_url":"https://arxiv.org/pdf/2609.37873","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/2609.37873","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128374282","display_name":"Subir Hait","orcid":"https://orcid.org/0009-0004-9871-9677","observed_orcids":["https://orcid.org/0009-0004-9871-9677"]},"institutions":[{"id":"https://openalex.org/I87216513","display_name":"Michigan State University","ror":"https://ror.org/05hs6h993","country_code":"US","type":"education","lineage":["https://openalex.org/I87216513"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hait, Subir","raw_affiliation_strings":["Department of Counseling, Educational Psychology, and Special Education , Michigan State University , 620 Farm Lane , East Lansing , MI 48824 , USA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Counseling, Educational Psychology, and Special Education , Michigan State University , 620 Farm Lane , East Lansing , MI 48824 , USA;","institution_ids":["https://openalex.org/I87216513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5128374282"],"corresponding_institution_ids":["https://openalex.org/I87216513"],"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.3578999936580658,"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":0.3578999936580658,"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":0.18140000104904175,"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":0.14229999482631683,"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/sequential-inference","display_name":"sequential inference","score":0.8169999718666077},{"id":"https://openalex.org/keywords/confidence-sequences","display_name":"confidence sequences","score":0.781000018119812},{"id":"https://openalex.org/keywords/anytime-valid-inference","display_name":"anytime-valid inference","score":0.7670000195503235},{"id":"https://openalex.org/keywords/neyman-allocation","display_name":"Neyman allocation","score":0.7289999723434448},{"id":"https://openalex.org/keywords/kullback-leibler-divergence","display_name":"Kullback-Leibler divergence","score":0.4050000011920929},{"id":"https://openalex.org/keywords/adaptive-tracking","display_name":"adaptive tracking","score":0.4659999907016754}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5758000016212463},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5688999891281128},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5594000220298767},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5091999769210815},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5066999793052673},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.4661000072956085},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.45399999618530273},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.43939998745918274},{"id":"https://openalex.org/C75373757","wikidata":"https://www.wikidata.org/wiki/Q7410160","display_name":"Sampling design","level":3,"score":0.4311000108718872},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.36399999260902405},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3517000079154968},{"id":"https://openalex.org/C2982777018","wikidata":"https://www.wikidata.org/wiki/Q99268086","display_name":"Adaptive design","level":3,"score":0.350600004196167},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.32440000772476196},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.32409998774528503},{"id":"https://openalex.org/C2983961125","wikidata":"https://www.wikidata.org/wiki/Q1288707","display_name":"Stopping rule","level":2,"score":0.31349998712539673},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.3125},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C78023250","wikidata":"https://www.wikidata.org/wiki/Q657596","display_name":"Unary operation","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C49898467","wikidata":"https://www.wikidata.org/wiki/Q1517706","display_name":"Stratified sampling","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3001999855041504},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C181789720","wikidata":"https://www.wikidata.org/wiki/Q4812191","display_name":"Asymptotically optimal algorithm","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.25870001316070557},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C154606282","wikidata":"https://www.wikidata.org/wiki/Q7048579","display_name":"Nominal level","level":3,"score":0.2538999915122986}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.37873","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37873","pdf_url":"https://arxiv.org/pdf/2609.37873","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.2609.37873","doi":"https://doi.org/10.48550/arxiv.2609.37873","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.37873","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.37873","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.37873","pdf_url":"https://arxiv.org/pdf/2609.37873","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/W7214967334.pdf","grobid_xml":"https://content.openalex.org/works/W7214967334.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W1977790907","https://openalex.org/W2027073833","https://openalex.org/W2032978307","https://openalex.org/W2037728958","https://openalex.org/W2039119502","https://openalex.org/W2059707463","https://openalex.org/W2127151679","https://openalex.org/W2280174025","https://openalex.org/W3030671780","https://openalex.org/W3087003407","https://openalex.org/W3126216366","https://openalex.org/W3159808052","https://openalex.org/W3190343448","https://openalex.org/W4388461534","https://openalex.org/W4402671999","https://openalex.org/W4404345312","https://openalex.org/W4405603083","https://openalex.org/W4406760119","https://openalex.org/W4415798641","https://openalex.org/W7160632553","https://openalex.org/W7167459552","https://openalex.org/W7169838747","https://openalex.org/W7169855593"],"related_works":["https://openalex.org/W2773886077","https://openalex.org/W3116073598","https://openalex.org/W4220729324","https://openalex.org/W7163991237","https://openalex.org/W2551117913","https://openalex.org/W4298421624","https://openalex.org/W4389260955","https://openalex.org/W784362094","https://openalex.org/W4321649904","https://openalex.org/W7099387631"],"abstract_inverted_index":{"Sequential":[0],"studies":[1],"with":[2,124,153],"heterogeneous":[3],"strata":[4],"may":[5],"define":[6],"the":[7,16,21,125,138,142,154,167],"estimand":[8],"under":[9],"a":[10,32,40,92,97,111,149,163],"target":[11,42,94],"mixture":[12],"that":[13,122],"differs":[14],"from":[15],"sampling":[17],"distribution.":[18],"We":[19,109],"organize":[20],"design":[22],"problem":[23,100],"by":[24],"guarantee":[25],"class:":[26],"one":[27],"pooled":[28,37,55,86],"confidence":[29],"sequence":[30],"for":[31,39,83,134,157],"single":[33],"prespecified":[34,41],"target,":[35],"shared":[36,63],"inference":[38,46,56],"set,":[43],"and":[44,61,65,101,116,130,162],"stratum-resolved":[45],"when":[47],"local":[48],"guarantees":[49],"must":[50],"remain":[51],"available.":[52],"For":[53],"target-set":[54],"we":[57],"derive":[58,110],"minimax,":[59],"gap-aware,":[60],"range-capped":[62],"proposals":[64],"an":[66,105,117],"adaptive":[67],"tracking":[68],"rule.":[69],"A":[70],"specialized":[71],"augmented":[72],"off-policy":[73],"score":[74],"removes":[75],"between-stratum":[76],"mean":[77],"heterogeneity":[78],"asymptotically,":[79],"recovering":[80],"Neyman":[81],"allocation":[82,151],"efficient":[84],"single-target":[85],"inference.":[87],"Robust":[88],"directional":[89],"certification":[90],"over":[91],"convex":[93],"set":[95],"is":[96],"classical":[98],"intersection-union":[99],"does":[102],"not":[103],"require":[104],"M-fold":[106],"vertex":[107],"split.":[108],"Gaussian":[112,139],"information":[113,121],"lower":[114],"bound":[115],"exact":[118],"finite-support":[119],"characteristic":[120],"coincides":[123],"bounded":[126],"nonparametric":[127],"KL-inf":[128],"limit,":[129],"state":[131],"sufficient":[132],"conditions":[133],"first-order":[135],"attainment":[136],"of":[137],"constant.":[140],"Within":[141],"local-CS":[143],"architecture,":[144],"regularly":[145],"varying":[146],"boundaries":[147],"imply":[148],"general":[150],"law":[152],"2/3":[155],"exponent":[156],"root-n":[158],"variance-adaptive":[159],"widths.":[160],"Simulations":[161],"PromptEval":[164],"replay":[165],"illustrate":[166],"resulting":[168],"guarantee-efficiency":[169],"frontier.":[170]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-10-01T00:00:00"}