{"id":"https://openalex.org/W7225451240","doi":"https://doi.org/10.1111/exsy.70471","title":"Interpretable Uplift Modelling: Conditional Average Treatment Effect Attribution, Subgroup Rules and Constrained Recourse","display_name":"Interpretable Uplift Modelling: Conditional Average Treatment Effect Attribution, Subgroup Rules and Constrained Recourse","publication_year":2026,"publication_date":"2026-11-06","ids":{"openalex":"https://openalex.org/W7225451240","doi":"https://doi.org/10.1111/exsy.70471"},"language":"en","primary_location":{"id":"doi:10.1111/exsy.70471","doi":"https://doi.org/10.1111/exsy.70471","is_oa":true,"landing_page_url":"https://doi.org/10.1111/exsy.70471","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.70471","source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-c","fnege-4","jufo-1","norway-1","poland-70","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.70471","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100433414","display_name":"Yu Zhang","orcid":"https://orcid.org/0000-0001-7889-2676","observed_orcids":["https://orcid.org/0000-0001-7889-2676","https://orcid.org/0009-0003-5368-1356","https://orcid.org/0009-0008-2673-9520"]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yu Zhang","raw_affiliation_strings":["Alibaba Group  Beijing China"],"raw_orcid":"https://orcid.org/0009-0003-5368-1356","affiliations":[{"raw_affiliation_string":"Alibaba Group  Beijing China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161160105","display_name":"Yueguang Shan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueguang Shan","raw_affiliation_strings":["Alibaba Group  Beijing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group  Beijing China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5161197889","display_name":"Wanlong Ma","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanlong Ma","raw_affiliation_strings":["Alibaba Group  Beijing China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group  Beijing China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100433414"],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":{"value":3600,"currency":"USD","value_usd":3600},"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"43","issue":"12","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9307000041007996,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9307000041007996,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.0608999989926815,"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/T12384","display_name":"Customer churn and segmentation","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/conditional-average-treatment-effect","display_name":"conditional average treatment effect","score":0.9829999804496765},{"id":"https://openalex.org/keywords/shapley-value","display_name":"Shapley value","score":0.9599999785423279},{"id":"https://openalex.org/keywords/subgroup-identification","display_name":"subgroup identification","score":0.7279999852180481},{"id":"https://openalex.org/keywords/recourse","display_name":"recourse","score":0.6710000038146973},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.6320000290870667},{"id":"https://openalex.org/keywords/effect-modifiers","display_name":"effect modifiers","score":0.5860000252723694},{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.5130000114440918}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7135000228881836},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.59579998254776},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5482000112533569},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.478300005197525},{"id":"https://openalex.org/C2780615836","wikidata":"https://www.wikidata.org/wiki/Q2471869","display_name":"USable","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.4083000123500824},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.40790000557899475},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4032000005245209},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37709999084472656},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3212999999523163},{"id":"https://openalex.org/C2987370644","wikidata":"https://www.wikidata.org/wiki/Q7836903","display_name":"Treatment effect","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.30979999899864197},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2624000012874603},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1111/exsy.70471","doi":"https://doi.org/10.1111/exsy.70471","is_oa":true,"landing_page_url":"https://doi.org/10.1111/exsy.70471","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.70471","source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-c","fnege-4","jufo-1","norway-1","poland-70","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1111/exsy.70471","doi":"https://doi.org/10.1111/exsy.70471","is_oa":true,"landing_page_url":"https://doi.org/10.1111/exsy.70471","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/exsy.70471","source":{"id":"https://openalex.org/S72232612","display_name":"Expert Systems","issn_l":"0266-4720","issn":["0266-4720","1468-0394"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","ccf-c","cwts-core","dongbi-c","fnege-4","jufo-1","norway-1","poland-70","russia-white-list-1"],"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225451240.pdf","grobid_xml":"https://content.openalex.org/works/W7225451240.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1671614046","https://openalex.org/W2064903582","https://openalex.org/W2208550830","https://openalex.org/W2282821441","https://openalex.org/W2305754340","https://openalex.org/W2338621248","https://openalex.org/W2624816748","https://openalex.org/W2730059782","https://openalex.org/W2945295328","https://openalex.org/W2964261049","https://openalex.org/W2973319951","https://openalex.org/W3089065092","https://openalex.org/W3211639647","https://openalex.org/W4210628929","https://openalex.org/W4385479903","https://openalex.org/W4388656403","https://openalex.org/W4391345489","https://openalex.org/W4394866983","https://openalex.org/W4400461591","https://openalex.org/W4400618112","https://openalex.org/W4401863657","https://openalex.org/W4406934726","https://openalex.org/W4408593087","https://openalex.org/W4411281867","https://openalex.org/W4416226814","https://openalex.org/W7125420728","https://openalex.org/W7133216959","https://openalex.org/W7137801928"],"related_works":["https://openalex.org/W7162154829","https://openalex.org/W7212260022","https://openalex.org/W7224742742","https://openalex.org/W4285238482","https://openalex.org/W4414696382","https://openalex.org/W2742407816","https://openalex.org/W7152227747","https://openalex.org/W3111052073","https://openalex.org/W7011708852","https://openalex.org/W7121075990"],"abstract_inverted_index":{"ABSTRACT":[0],"Uplift":[1],"models":[2],"estimate":[3],"how":[4],"treatment":[5,37],"effects":[6],"vary":[7],"across":[8],"individuals,":[9],"but":[10],"a":[11,70],"prediction":[12],"alone":[13],"does":[14,117,137],"not":[15,118,138,166],"explain":[16],"feature":[17,25],"contributions,":[18],"define":[19],"usable":[20],"subgroups,":[21],"or":[22,181],"suggest":[23],"alternative":[24],"states.":[26,183],"We":[27],"combine":[28],"these":[29,90,104],"three":[30],"outputs":[31,91,155],"through":[32],"one":[33],"fitted":[34,97],"conditional":[35],"average":[36],"effect":[38,94],"(CATE)":[39],"model.":[40,54],"Treatment":[41],"Effect":[42],"Shapley":[43,49],"Values":[44],"(TESV)":[45],"applies":[46],"existing":[47],"interventional":[48],"attribution":[50],"directly":[51],"to":[52],"this":[53],"Stable":[55],"Causal":[56],"Rule":[57,123],"Learning":[58],"(SCRL)":[59],"produces":[60],"concise":[61],"subgroup":[62],"descriptions":[63],"using":[64],"empirical":[65],"treatment\u2013control":[66],"support":[67,163],"checks":[68,164],"and":[69,99,129,132,170],"bootstrap\u2010based":[71],"score.":[72],"Constrained":[73],"feature\u2010space":[74],"recourse":[75,171],"searches":[76],"for":[77],"higher":[78,140],"predicted":[79],"CATE":[80],"under":[81],"permitted":[82],"coordinate":[83],"changes.":[84],"Across":[85],"four":[86],"benchmarks,":[87],"we":[88],"evaluate":[89],"against":[92],"known":[93],"modifiers,":[95],"the":[96,113,127,133,144],"model,":[98],"held\u2010out":[100],"outcome":[101],"contrasts,":[102],"keeping":[103],"reference":[105],"targets":[106,172],"distinct.":[107],"Attribution":[108],"reflects":[109],"dependencies":[110],"learned":[111,120],"by":[112],"model;":[114],"marginal":[115],"replacement":[116],"remove":[119],"proxy":[121],"dependence.":[122],"quality":[124],"varies":[125],"with":[126],"dataset":[128],"evaluation":[130,136],"target,":[131],"Hillstrom":[134],"randomised":[135],"establish":[139,167],"per\u2010contact":[141],"efficacy":[142],"than":[143],"model's":[145],"ranking.":[146],"The":[147],"framework":[148],"therefore":[149],"supports":[150],"model":[151],"inspection,":[152],"while":[153],"its":[154],"require":[156],"further":[157],"evidence":[158],"before":[159],"guiding":[160],"interventions.":[161],"Empirical":[162],"do":[165],"causal":[168],"identification,":[169],"are":[173],"neither":[174],"validated":[175],"actions":[176],"nor":[177],"guaranteed":[178],"jointly":[179],"plausible":[180],"reachable":[182]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-11-08T00:00:00"}