{"id":"https://openalex.org/W4414200532","doi":"https://doi.org/10.1007/s10994-025-06880-4","title":"Counterfactual ensembles for interpretable churn prediction: from real-world to privacy-preserving synthetic data","display_name":"Counterfactual ensembles for interpretable churn prediction: from real-world to privacy-preserving synthetic data","publication_year":2025,"publication_date":"2025-09-15","ids":{"openalex":"https://openalex.org/W4414200532","doi":"https://doi.org/10.1007/s10994-025-06880-4"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-025-06880-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06880-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06880-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06880-4.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116040877","display_name":"Samuele Tonati","orcid":null},"institutions":[{"id":"https://openalex.org/I157210198","display_name":"Scuola Normale Superiore","ror":"https://ror.org/03aydme10","country_code":"IT","type":"education","lineage":["https://openalex.org/I157210198"]},{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Samuele Tonati","raw_affiliation_strings":["Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy","University of Pisa, Lungarno Antonio Pacinotti 43, 56126, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy","institution_ids":["https://openalex.org/I157210198"]},{"raw_affiliation_string":"University of Pisa, Lungarno Antonio Pacinotti 43, 56126, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054644292","display_name":"Marzio Di Vece","orcid":"https://orcid.org/0000-0002-8884-5432"},"institutions":[{"id":"https://openalex.org/I157210198","display_name":"Scuola Normale Superiore","ror":"https://ror.org/03aydme10","country_code":"IT","type":"education","lineage":["https://openalex.org/I157210198"]},{"id":"https://openalex.org/I127077003","display_name":"IMT School for Advanced Studies Lucca","ror":"https://ror.org/035gh3a49","country_code":"IT","type":"education","lineage":["https://openalex.org/I127077003"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Marzio Di Vece","raw_affiliation_strings":["IMT School for Advanced Studies, Piazza San Francesco 19, 55100, Lucca, Italy","Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"IMT School for Advanced Studies, Piazza San Francesco 19, 55100, Lucca, Italy","institution_ids":["https://openalex.org/I127077003"]},{"raw_affiliation_string":"Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy","institution_ids":["https://openalex.org/I157210198"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024505700","display_name":"Fosca Giannotti","orcid":"https://orcid.org/0000-0003-3099-3835"},"institutions":[{"id":"https://openalex.org/I157210198","display_name":"Scuola Normale Superiore","ror":"https://ror.org/03aydme10","country_code":"IT","type":"education","lineage":["https://openalex.org/I157210198"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Fosca Giannotti","raw_affiliation_strings":["Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy","institution_ids":["https://openalex.org/I157210198"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012682000","display_name":"Roberto Pellungrini","orcid":"https://orcid.org/0000-0003-3268-9271"},"institutions":[{"id":"https://openalex.org/I157210198","display_name":"Scuola Normale Superiore","ror":"https://ror.org/03aydme10","country_code":"IT","type":"education","lineage":["https://openalex.org/I157210198"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Roberto Pellungrini","raw_affiliation_strings":["Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy"],"affiliations":[{"raw_affiliation_string":"Scuola Normale Superiore, Piazza dei Cavalieri 7, 56126, Pisa, Italy","institution_ids":["https://openalex.org/I157210198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5116040877"],"corresponding_institution_ids":["https://openalex.org/I108290504","https://openalex.org/I157210198"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32519792,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"114","issue":"10","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9835000038146973,"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"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9835000038146973,"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"}},{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.9678999781608582,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.957099974155426,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/counterfactual-conditional","display_name":"Counterfactual conditional","score":0.8952000141143799},{"id":"https://openalex.org/keywords/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.8371999859809875},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.6740999817848206},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.6725000143051147},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6625999808311462},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5774000287055969},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4616999924182892}],"concepts":[{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.8952000141143799},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.8371999859809875},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7652999758720398},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.6740999817848206},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.6725000143051147},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6625999808311462},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6428999900817871},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6337000131607056},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5774000287055969},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39640000462532043},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.38269999623298645},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.3718000054359436},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10994-025-06880-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06880-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06880-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:arpi.unipi.it:11568/1325071","is_oa":false,"landing_page_url":"https://hdl.handle.net/11568/1325071","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:ricerca.sns.it:11384/156684","is_oa":true,"landing_page_url":"https://hdl.handle.net/11384/156684","pdf_url":null,"source":{"id":"https://openalex.org/S7407050981","display_name":"Scuola Normale Superiore di Pisa","issn_l":null,"issn":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s10994-025-06880-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-025-06880-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-025-06880-4.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1016895160","display_name":null,"funder_award_id":"PNRR-M4C2","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G2020622295","display_name":null,"funder_award_id":"Big Data","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G2070155107","display_name":"It takes two to tango: a synergistic approach to human-machine decision making","funder_award_id":"101120763","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G3650733657","display_name":null,"funder_award_id":"NextGenerationEU","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4317964049","display_name":null,"funder_award_id":"IR0000013","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G4508289328","display_name":null,"funder_award_id":"PE00000013","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G5538284277","display_name":null,"funder_award_id":"National Recovery","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G7318860614","display_name":null,"funder_award_id":"834756","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8051717526","display_name":null,"funder_award_id":"Grant","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8409961469","display_name":null,"funder_award_id":"Spoke 1","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8657926182","display_name":null,"funder_award_id":"ERC-2018-ADG G.A","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"},{"id":"https://openalex.org/G8893660128","display_name":null,"funder_award_id":"PE0000001","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414200532.pdf","grobid_xml":"https://content.openalex.org/works/W4414200532.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1571694585","https://openalex.org/W2067594023","https://openalex.org/W2295598076","https://openalex.org/W2422895071","https://openalex.org/W2565167788","https://openalex.org/W2577756876","https://openalex.org/W2779683208","https://openalex.org/W2806801170","https://openalex.org/W2945151003","https://openalex.org/W2945295328","https://openalex.org/W2964449086","https://openalex.org/W2966362896","https://openalex.org/W2992634800","https://openalex.org/W3033682799","https://openalex.org/W3082154094","https://openalex.org/W3104149808","https://openalex.org/W3125997628","https://openalex.org/W3130450512","https://openalex.org/W3130571284","https://openalex.org/W3175645274","https://openalex.org/W3195311662","https://openalex.org/W3204818612","https://openalex.org/W3207900600","https://openalex.org/W3208743394","https://openalex.org/W3210519732","https://openalex.org/W4207021985","https://openalex.org/W4214736071","https://openalex.org/W4225150645","https://openalex.org/W4285123252","https://openalex.org/W4285794405","https://openalex.org/W4319586157","https://openalex.org/W4321786089","https://openalex.org/W4366262984","https://openalex.org/W4390618104","https://openalex.org/W4394712909","https://openalex.org/W4396941396","https://openalex.org/W4399932993","https://openalex.org/W4400700727","https://openalex.org/W4403173041","https://openalex.org/W4403844673","https://openalex.org/W4408285914","https://openalex.org/W4411337368"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Counterfactual":[1],"explanations":[2],"identify":[3],"minimal":[4],"input":[5],"changes":[6],"needed":[7],"to":[8,99],"alter":[9],"a":[10,56],"machine":[11],"learning":[12],"model\u2019s":[13],"prediction,":[14,90],"offering":[15],"actionable":[16],"insights":[17],"in":[18,31],"tasks":[19],"like":[20],"churn":[21,73,89,111],"analysis.":[22],"However,":[23],"existing":[24],"methods":[25],"often":[26],"produce":[27],"counterfactuals":[28,92,125],"that":[29,46,114],"vary":[30],"quality,":[32],"coherence,":[33],"and":[34,51,86,122,129,135],"plausibility,":[35],"limiting":[36],"their":[37,53],"practical":[38],"value.":[39],"We":[40],"propose":[41],"an":[42],"ensemble":[43,116],"evaluation":[44],"framework":[45],"integrates":[47],"multiple":[48,61],"generation":[49],"techniques":[50],"ranks":[52],"outputs":[54],"using":[55],"tunable":[57],"scoring":[58,80],"function":[59],"balancing":[60],"relevant":[62],"metrics.":[63],"Our":[64],"approach":[65,117],"addresses":[66],"two":[67],"key":[68],"deployment":[69],"scenarios:":[70],"(i)":[71],"in-house":[72],"analysis,":[74],"where":[75,91],"decision-makers":[76],"can":[77],"interactively":[78],"adjust":[79],"weights":[81],"for":[82],"tailored,":[83],"user-driven":[84],"explanations;":[85],"(ii)":[87],"outsourced":[88],"must":[93],"be":[94],"generated":[95],"on":[96,109],"synthetic":[97,130],"data":[98],"preserve":[100],"privacy":[101],"while":[102],"remaining":[103],"representative":[104],"of":[105,124],"real":[106,128],"cases.":[107],"Experiments":[108],"benchmark":[110],"datasets":[112],"demonstrate":[113],"our":[115],"improves":[118],"the":[119],"consistency,":[120],"interpretability,":[121],"utility":[123],"across":[126],"both":[127],"settings,":[131],"supporting":[132],"more":[133],"reliable":[134],"privacy-aware":[136],"decision-making.":[137]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}