{"id":"https://openalex.org/W6929350958","doi":"https://doi.org/10.48550/arxiv.2503.13912","title":"KANITE: Kolmogorov-Arnold Networks for ITE estimation","display_name":"KANITE: Kolmogorov-Arnold Networks for ITE estimation","publication_year":2025,"publication_date":"2025-03-18","ids":{"openalex":"https://openalex.org/W6929350958","doi":"https://doi.org/10.48550/arxiv.2503.13912"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2503.13912","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.13912","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.2503.13912","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mehendale, Eshan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mehendale, Eshan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Thorat, Abhinav","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thorat, Abhinav","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Kolla, Ravi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kolla, Ravi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Pedanekar, Niranjan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pedanekar, Niranjan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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":true,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.760699987411499,"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":0.760699987411499,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.04540000110864639,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.04190000146627426,"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/causal-inference","display_name":"Causal inference","score":0.5885999798774719},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5368000268936157},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5231999754905701},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5084999799728394},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.44429999589920044},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.44279998540878296},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.40230000019073486},{"id":"https://openalex.org/keywords/univariate","display_name":"Univariate","score":0.39989998936653137},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.37119999527931213},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.34700000286102295}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6621999740600586},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.5885999798774719},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5874000191688538},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5368000268936157},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5231999754905701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5210000276565552},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5084999799728394},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.44429999589920044},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.44279998540878296},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C199163554","wikidata":"https://www.wikidata.org/wiki/Q1681619","display_name":"Univariate","level":3,"score":0.39989998936653137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39149999618530273},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.31029999256134033},{"id":"https://openalex.org/C2983703474","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Probability estimation","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.24819999933242798},{"id":"https://openalex.org/C95546049","wikidata":"https://www.wikidata.org/wiki/Q1345207","display_name":"Entropy estimation","level":3,"score":0.2468000054359436},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.24639999866485596},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.2395000010728836},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.23929999768733978},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2273000031709671},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.22609999775886536},{"id":"https://openalex.org/C106752470","wikidata":"https://www.wikidata.org/wiki/Q1364826","display_name":"Joint entropy","level":3,"score":0.2159000039100647},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.21359999477863312},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.2070000022649765},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.20509999990463257},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.20350000262260437},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.1995999962091446},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.19629999995231628},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.1940000057220459},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.19089999794960022},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.19030000269412994},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.18569999933242798},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.18459999561309814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1826999932527542},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.18250000476837158}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2503.13912","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.13912","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.2503.13912","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.13912","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"KANITE,":[2],"a":[3,66],"framework":[4,50],"leveraging":[5],"Kolmogorov-Arnold":[6],"Networks":[7],"(KANs)":[8],"for":[9,86],"Individual":[10],"Treatment":[11],"Effect":[12],"(ITE)":[13],"estimation":[14,74],"under":[15],"multiple":[16,76],"treatments":[17],"setting":[18],"in":[19,65,113,126],"causal":[20,129,138],"inference.":[21],"By":[22],"utilizing":[23],"KAN's":[24],"unique":[25],"abilities":[26],"to":[27,34,69,95,136],"learn":[28],"univariate":[29],"activation":[30],"functions":[31],"as":[32],"opposed":[33],"learning":[35],"linear":[36],"weights":[37,85],"by":[38,91],"Multi-Layer":[39],"Perceptrons":[40],"(MLPs),":[41],"we":[42],"improve":[43],"the":[44,97,122,132],"estimates":[45],"of":[46,124,134],"ITEs.":[47],"The":[48],"KANITE":[49,109,125],"comprises":[51],"two":[52],"key":[53],"architectures:":[54],"1.Integral":[55],"Probability":[56],"Metric":[57],"(IPM)":[58],"architecture:":[59,82],"This":[60,83],"employs":[61],"an":[62],"IPM":[63],"loss":[64],"specialized":[67],"manner":[68],"effectively":[70],"align":[71],"towards":[72],"ITE":[73],"across":[75,99,141],"treatments.":[77],"2.":[78],"Entropy":[79],"Balancing":[80],"(EB)":[81],"uses":[84],"samples":[87],"that":[88,108],"are":[89],"learned":[90],"optimizing":[92],"entropy":[93],"subject":[94],"balancing":[96],"covariates":[98],"treatment":[100],"groups.":[101],"Extensive":[102],"evaluations":[103],"on":[104],"benchmark":[105],"datasets":[106],"demonstrate":[107],"outperforms":[110],"state-of-the-art":[111],"algorithms":[112],"both":[114],"$\u03b5_{\\text{PEHE}}$":[115],"and":[116],"$\u03b5_{\\text{ATE}}$":[117],"metrics.":[118],"Our":[119],"experiments":[120],"highlight":[121],"advantages":[123],"achieving":[127],"improved":[128],"estimates,":[130],"emphasizing":[131],"potential":[133],"KANs":[135],"advance":[137],"inference":[139],"methodologies":[140],"diverse":[142],"application":[143],"areas.":[144]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
