{"id":"https://openalex.org/W7153226266","doi":"https://doi.org/10.48550/arxiv.2604.07513","title":"SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation","display_name":"SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7153226266","doi":"https://doi.org/10.48550/arxiv.2604.07513"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.07513","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07513","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.07513","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120599435","display_name":"Grace Jiarui Fan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Fan, Grace Jiarui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111496462","display_name":"Chengpiao Huang","orcid":"https://orcid.org/0009-0008-2193-7632"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Chengpiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002767768","display_name":"Tianyi Peng","orcid":"https://orcid.org/0000-0002-9046-3206"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Tianyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133326628","display_name":"Kaizheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Kaizheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5007826385","display_name":"Yuhang Wu","orcid":"https://orcid.org/0009-0007-9214-4392"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yuhang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5120599435"],"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/T14074","display_name":"Persona Design and Applications","score":0.3506999909877777,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14074","display_name":"Persona Design and Applications","score":0.3506999909877777,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.044199999421834946,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12501","display_name":"Digital Economy and Work Transformation","score":0.03700000047683716,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.7024000287055969},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4945000112056732},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.42149999737739563},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.392300009727478},{"id":"https://openalex.org/keywords/persona","display_name":"Persona","score":0.3767000138759613},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.35429999232292175},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.3156999945640564}],"concepts":[{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.7024000287055969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6672000288963318},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4754999876022339},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4081000089645386},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C313442","wikidata":"https://www.wikidata.org/wiki/Q778556","display_name":"Persona","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3467999994754791},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3102000057697296},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2883000075817108},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2603999972343445},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.25119999051094055},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.07513","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07513","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.07513","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07513","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"AI-based":[0],"persona":[1,129],"simulation":[2,10,142],"--":[3,11],"often":[4,30],"referred":[5],"to":[6,37,79,160,175],"as":[7,88],"digital":[8],"twin":[9],"is":[12,100],"increasingly":[13],"used":[14],"for":[15,58,143],"market":[16],"research,":[17],"recommender":[18],"systems,":[19],"and":[20,34,65,76,98,111,120,133,140,147,167],"social":[21],"sciences.":[22],"Despite":[23],"their":[24,42],"flexibility,":[25],"large":[26],"language":[27],"models":[28],"(LLMs)":[29],"exhibit":[31],"systematic":[32],"bias":[33],"miscalibration":[35],"relative":[36,162,169],"real":[38],"human":[39,83],"behavior,":[40],"limiting":[41],"reliability.":[43],"Inspired":[44],"by":[45],"synthetic":[46],"control":[47],"methods":[48,126],"from":[49,73],"causal":[50],"inference,":[51],"we":[52,121],"propose":[53],"SYN-DIGITS":[54,86,136,157],"(SYNthetic":[55],"Control":[56],"Framework":[57],"Calibrated":[59],"DIGItal":[60],"Twin":[61],"Simulation),":[62],"a":[63,89,104],"principled":[64],"lightweight":[66],"calibration":[67,113,125],"framework":[68],"that":[69,108,156],"learns":[70],"latent":[71,105,116],"structure":[72],"digital-twin":[74],"responses":[75],"transfers":[77],"it":[78],"align":[80],"predictions":[81],"with":[82,150],"ground":[84],"truth.":[85],"operates":[87],"post-processing":[90],"layer":[91],"on":[92],"top":[93],"of":[94],"any":[95],"LLM-based":[96],"simulator":[97],"thus":[99],"model-agnostic.":[101],"We":[102],"develop":[103],"factor":[106],"model":[107],"formalizes":[109],"when":[110],"why":[112],"succeeds":[114],"through":[115],"space":[117],"alignment":[118],"conditions,":[119],"systematically":[122],"evaluate":[123],"ten":[124],"across":[127],"thirteen":[128],"constructions,":[130],"three":[131],"LLMs,":[132],"two":[134],"datasets.":[135],"supports":[137],"both":[138],"individual-level":[139,165],"distributional":[141,172],"previously":[144],"unseen":[145],"questions":[146],"unobserved":[148],"populations,":[149],"provable":[151],"error":[152],"guarantees.":[153],"Experiments":[154],"show":[155],"achieves":[158],"up":[159],"50%":[161],"improvements":[163],"in":[164,171],"correlation":[166],"50--90%":[168],"reductions":[170],"discrepancy":[173],"compared":[174],"uncalibrated":[176],"baselines.":[177]},"counts_by_year":[],"updated_date":"2026-04-11T06:19:08.300824","created_date":"2026-04-11T00:00:00"}
