{"id":"https://openalex.org/W7224531971","doi":"https://doi.org/10.18653/2026.emnlp-main.2390","title":"CauTab: Counterfactual Tabular Data Synthesis with Functional and Causal-Dependency Constraints","display_name":"CauTab: Counterfactual Tabular Data Synthesis with Functional and Causal-Dependency Constraints","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7224531971","doi":"https://doi.org/10.18653/2026.emnlp-main.2390"},"language":"en","primary_location":{"id":"doi:10.18653/2026.emnlp-main.2390","doi":"https://doi.org/10.18653/2026.emnlp-main.2390","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.2390","pdf_url":"https://aclanthology.org/2026.emnlp-main.2390.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.emnlp-main.2390.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5160096941","display_name":"Xingyu Yang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xingyu Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111188748","display_name":"Minmin Ma","orcid":"https://orcid.org/0009-0000-5980-567X","observed_orcids":["https://orcid.org/0009-0000-5980-567X","https://orcid.org/0009-0007-3546-3573"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mingchuan Ma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069390870","display_name":"Dexuan Xu","orcid":"https://orcid.org/0000-0003-3100-5886","observed_orcids":["https://orcid.org/0000-0003-3100-5886"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dexuan Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160130791","display_name":"Xiangyang Long","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangyang Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055069752","display_name":"Yu Huang","orcid":"https://orcid.org/0000-0003-2730-5077","observed_orcids":["https://orcid.org/0000-0003-2730-5077"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu Huang","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":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76742975,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"54342","last_page":"54363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.6015999913215637,"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"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.6015999913215637,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0835999995470047,"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/T10028","display_name":"Topic Modeling","score":0.03229999914765358,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/tabular-data-synthesis","display_name":"tabular data synthesis","score":0.972000002861023},{"id":"https://openalex.org/keywords/synthetic-data-generation","display_name":"synthetic data generation","score":0.7870000004768372},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"counterfactual reasoning","score":0.8539999723434448},{"id":"https://openalex.org/keywords/functional-dependencies","display_name":"functional dependencies","score":0.9789999723434448},{"id":"https://openalex.org/keywords/logical-consistency","display_name":"logical consistency","score":0.7229999899864197},{"id":"https://openalex.org/keywords/privacy-preserving-data-generation","display_name":"privacy-preserving data generation","score":0.7630000114440918},{"id":"https://openalex.org/keywords/deep-generative-models","display_name":"deep generative models","score":0.722000002861023}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.6225000023841858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5566999912261963},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4065000116825104},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.33799999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3248000144958496},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31790000200271606},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2671000063419342},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2578999996185303},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.2572999894618988}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/2026.emnlp-main.2390","doi":"https://doi.org/10.18653/2026.emnlp-main.2390","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.2390","pdf_url":"https://aclanthology.org/2026.emnlp-main.2390.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/2026.emnlp-main.2390","doi":"https://doi.org/10.18653/2026.emnlp-main.2390","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.2390","pdf_url":"https://aclanthology.org/2026.emnlp-main.2390.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7224531971.pdf","grobid_xml":"https://content.openalex.org/works/W7224531971.grobid-xml"},"referenced_works_count":13,"referenced_works":["https://openalex.org/W4283388679","https://openalex.org/W4306178203","https://openalex.org/W4367692219","https://openalex.org/W4383473763","https://openalex.org/W4383987205","https://openalex.org/W4387723908","https://openalex.org/W4399794317","https://openalex.org/W4412887758","https://openalex.org/W4414593579","https://openalex.org/W4416035155","https://openalex.org/W4416409141","https://openalex.org/W4417105535","https://openalex.org/W6982217168"],"related_works":["https://openalex.org/W7160178997","https://openalex.org/W7155197907","https://openalex.org/W4389520364","https://openalex.org/W7224530669","https://openalex.org/W6903787653","https://openalex.org/W7166842029","https://openalex.org/W7119914782","https://openalex.org/W2970453125","https://openalex.org/W2972614864","https://openalex.org/W4407902187"],"abstract_inverted_index":{"High-quality":[0],"tabular":[1,60],"data":[2,17,40,61,138],"is":[3],"essential":[4],"for":[5,59,121],"datadriven":[6],"tasks,":[7],"yet":[8],"often":[9],"limited":[10],"by":[11,19,93],"scarcity":[12],"and":[13,24,67,101,116,140,147],"privacy":[14,146],"concerns.While":[15],"synthetic":[16],"generated":[18,39],"recent":[20],"Deep":[21],"Generative":[22],"Models":[23],"LLMs":[25],"improves":[26,125],"fidelity,":[27,141],"existing":[28],"methods":[29],"struggle":[30],"to":[31,82],"preserve":[32],"rigorous":[33],"attribute":[34],"dependencies.As":[35],"a":[36,55,72,90,105],"result,":[37],"the":[38,110,118],"may":[41],"appear":[42],"statistically":[43],"realistic":[44],"but":[45],"lack":[46],"logical":[47,130,149],"consistency.To":[48],"address":[49],"this":[50],"challenge,":[51],"we":[52],"propose":[53],"CauTab,":[54],"novel":[56],"counterfactual":[57],"framework":[58],"synthesis":[62],"that":[63,133],"preserves":[64],"functional":[65,85],"dependencies":[66],"causal-dependency":[68,91],"structures.CauTab":[69],"operates":[70],"in":[71,137],"three-stage":[73],"pipeline:":[74],"(1)":[75],"it":[76,88,103],"employs":[77],"an":[78],"LLM-guided":[79],"mining":[80],"strategy":[81],"efficiently":[83],"identify":[84],"dependencies;":[86],"(2)":[87],"constructs":[89],"graph":[92,120],"integrating":[94],"data-driven":[95],"optimization":[96],"with":[97],"LLM-based":[98],"semantic":[99],"reasoning;":[100],"(3)":[102],"executes":[104],"dependency-constrained":[106],"generation":[107],"process":[108],"via":[109],"Abduction-Intervention-Prediction":[111],"paradigm.By":[112],"enforcing":[113],"deterministic":[114],"constraints":[115],"using":[117],"learned":[119],"structured":[122],"propagation,":[123],"CauTab":[124,134],"statistical":[126],"fidelity":[127],"while":[128,142],"preserving":[129],"consistency.Experiments":[131],"demonstrate":[132],"outperforms":[135],"baselines":[136],"utility":[139],"maintaining":[143],"competitive":[144],"empirical":[145],"strong":[148],"consistency.":[150]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-10-30T00:00:00"}