{"id":"https://openalex.org/W7134042587","doi":"https://doi.org/10.48550/arxiv.2603.05396","title":"Harnessing Synthetic Data from Generative AI for Statistical Inference","display_name":"Harnessing Synthetic Data from Generative AI for Statistical Inference","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134042587","doi":"https://doi.org/10.48550/arxiv.2603.05396"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.05396","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128242065","display_name":"Ahmad Abdel-Azim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdel-Azim, Ahmad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128231584","display_name":"Ruoyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ruoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128247035","display_name":"Xihong Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Xihong","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28523342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.20440000295639038,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.20440000295639038,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.041600000113248825,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10848","display_name":"Advanced Multi-Objective Optimization Algorithms","score":0.04100000113248825,"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/synthetic-data","display_name":"Synthetic data","score":0.7181000113487244},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.6445000171661377},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5415999889373779},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5214999914169312},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.516700029373169},{"id":"https://openalex.org/keywords/data-driven","display_name":"Data-driven","score":0.37709999084472656}],"concepts":[{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.7181000113487244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6801000237464905},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.6445000171661377},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5415999889373779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5221999883651733},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5214999914169312},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.516700029373169},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.42829999327659607},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42489999532699585},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.37709999084472656},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C2780535194","wikidata":"https://www.wikidata.org/wiki/Q309901","display_name":"Open data","level":2,"score":0.2921999990940094},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C2778464652","wikidata":"https://www.wikidata.org/wiki/Q309849","display_name":"Open research","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.05396","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.05396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05396","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":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.05396","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.5769453048706055,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"emergence":[1],"of":[2,13,57,70,90,152],"generative":[3,92],"AI":[4],"models":[5],"has":[6],"dramatically":[7],"expanded":[8],"the":[9,54,68,72,99,149],"availability":[10],"and":[11,19,48,61,84,98,108,136,162,170],"use":[12,62,96,151],"synthetic":[14,39,58,76,120,153],"data":[15,29,40,59,77,121],"across":[16],"scientific,":[17],"industrial,":[18],"policy":[20],"domains.":[21],"While":[22],"these":[23,142],"developments":[24],"open":[25,160],"new":[26],"possibilities":[27],"for":[28,126,148],"analysis,":[30],"they":[31,101],"also":[32,104],"raise":[33],"fundamental":[34],"statistical":[35,65],"questions":[36],"about":[37],"when":[38,119],"can":[41,78],"be":[42],"used":[43],"in":[44,138],"a":[45,64],"valid,":[46],"reliable,":[47],"principled":[49,150],"manner.":[50],"This":[51],"paper":[52],"reviews":[53],"current":[55],"landscape":[56],"generation":[60],"from":[63,131],"perspective,":[66],"with":[67,157],"goal":[69],"clarifying":[71],"assumptions":[73],"under":[74],"which":[75],"meaningfully":[79],"support":[80],"downstream":[81],"discovery,":[82],"inference,":[83],"prediction.":[85],"We":[86,112,155],"survey":[87],"major":[88],"classes":[89],"modern":[91],"models,":[93],"their":[94,106],"intended":[95,164],"cases,":[97],"benefits":[100],"offer,":[102],"while":[103],"highlighting":[105],"limitations":[107],"characteristic":[109],"failure":[110],"modes.":[111],"additionally":[113],"examine":[114],"common":[115],"pitfalls":[116],"that":[117],"arise":[118],"are":[122],"treated":[123],"as":[124],"surrogates":[125],"real":[127],"observations,":[128],"including":[129],"biases":[130],"model":[132],"misspecification,":[133],"attenuated":[134],"uncertainty,":[135],"difficulties":[137],"generalization.":[139],"Building":[140],"on":[141],"insights,":[143],"we":[144],"discuss":[145],"emerging":[146],"frameworks":[147],"data.":[154],"conclude":[156],"practical":[158],"recommendations,":[159],"problems,":[161],"cautions":[163],"to":[165],"guide":[166],"both":[167],"method":[168],"developers":[169],"applied":[171],"researchers.":[172]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-07T00:00:00"}
