{"id":"https://openalex.org/W7124743408","doi":"https://doi.org/10.1145/3787464","title":"Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation","display_name":"Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation","publication_year":2026,"publication_date":"2026-01-19","ids":{"openalex":"https://openalex.org/W7124743408","doi":"https://doi.org/10.1145/3787464","pmid":"https://pubmed.ncbi.nlm.nih.gov/42022184"},"language":"en","primary_location":{"id":"doi:10.1145/3787464","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3787464","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13099081/","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123306312","display_name":"Changchang Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Changchang Yin","raw_affiliation_strings":["The Ohio State University, Columbus, Ohio, USA"],"raw_orcid":"https://orcid.org/0000-0002-6540-6365","affiliations":[{"raw_affiliation_string":"The Ohio State University, Columbus, Ohio, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123324868","display_name":"Hong-You Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hong-You Chen","raw_affiliation_strings":["The Ohio State University, Columbus, Ohio, USA"],"raw_orcid":"https://orcid.org/0009-0001-6408-9097","affiliations":[{"raw_affiliation_string":"The Ohio State University, Columbus, Ohio, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123300788","display_name":"Wei-Lun Chao","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei-Lun Chao","raw_affiliation_strings":["The Ohio State University, Columbus, Ohio, USA"],"raw_orcid":"https://orcid.org/0000-0003-1269-7231","affiliations":[{"raw_affiliation_string":"The Ohio State University, Columbus, Ohio, USA","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123353408","display_name":"Ping Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ping Zhang","raw_affiliation_strings":["The Ohio State University, Columbus, Ohio, USA"],"raw_orcid":"https://orcid.org/0000-0002-4601-0779","affiliations":[{"raw_affiliation_string":"The Ohio State University, Columbus, Ohio, USA","institution_ids":["https://openalex.org/I52357470"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52357470"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0753121,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":"2","first_page":"1","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.4611999988555908,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.4611999988555908,"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":0.299699991941452,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.04490000009536743,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6281999945640564},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.6037999987602234},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.5871000289916992},{"id":"https://openalex.org/keywords/average-treatment-effect","display_name":"Average treatment effect","score":0.5037000179290771},{"id":"https://openalex.org/keywords/conditional-probability","display_name":"Conditional probability","score":0.4636000096797943},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.44620001316070557},{"id":"https://openalex.org/keywords/inverse-probability-weighting","display_name":"Inverse probability weighting","score":0.4334999918937683},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3741999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8255000114440918},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6281999945640564},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.6037999987602234},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.5037000179290771},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.4636000096797943},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C2779915747","wikidata":"https://www.wikidata.org/wiki/Q17058619","display_name":"Inverse probability weighting","level":3,"score":0.4334999918937683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4327000081539154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3831999897956848},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37630000710487366},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C35981017","wikidata":"https://www.wikidata.org/wiki/Q6060409","display_name":"Inverse probability","level":4,"score":0.33309999108314514},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3280999958515167},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2897000014781952},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3787464","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3787464","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Intelligent Systems and Technology","raw_type":"journal-article"},{"id":"pmid:42022184","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42022184","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM transactions on intelligent systems and technology","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:13099081","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13099081/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACM Trans Intell Syst Technol","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13099081","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13099081/","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ACM Trans Intell Syst Technol","raw_type":"Text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2681174601","display_name":null,"funder_award_id":"R01AI188576","funder_id":"https://openalex.org/F4320337355","funder_display_name":"National Institute of Allergy and Infectious Diseases"}],"funders":[{"id":"https://openalex.org/F4320337355","display_name":"National Institute of Allergy and Infectious Diseases","ror":"https://ror.org/043z4tv69"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2150291618","https://openalex.org/W2624816748","https://openalex.org/W3021654819","https://openalex.org/W3121380333","https://openalex.org/W3124999902"],"related_works":[],"abstract_inverted_index":{"Individual":[0],"Treatment":[1,112],"Effect":[2],"(ITE)":[3],"estimation":[4,28,70,256],"is":[5,19,109,137],"to":[6,50,79,88,125,173],"evaluate":[7],"the":[8,40,51,59,68,81,89,94,117,123,148,186,203,207,223,239,249,259],"causal":[9],"effects":[10,209],"of":[11,120,205,210],"treatment":[12,121,208,263],"strategies":[13],"on":[14,202,213,231,247,251],"some":[15],"important":[16],"outcomes,":[17],"which":[18,57,76,115],"a":[20,73,97,133,144,170,177],"crucial":[21],"problem":[22],"in":[23,36,72,93,132,222,266],"healthcare.":[24],"Most":[25],"existing":[26],"ITE":[27,69,255],"methods":[29,60,246],"are":[30,43],"designed":[31],"for":[32,217,261],"centralized":[33],"settings.":[34],"However,":[35],"real-world":[37,235],"clinical":[38],"scenarios,":[39],"raw":[41],"data":[42,83,156],"usually":[44],"not":[45,61],"shareable":[46],"among":[47],"hospitals":[48],"due":[49],"potential":[52],"privacy":[53],"and":[54,188,196,234,238,254],"security":[55],"risks,":[56],"makes":[58],"applicable.":[62],"In":[63],"this":[64],"work,":[65],"we":[66,167],"study":[67],"task":[71,204],"federated":[74,134,178],"setting,":[75,135],"allows":[77],"us":[78],"harness":[80],"decentralized":[82],"from":[84,101,161],"multiple":[85],"hospitals.":[86],"Due":[87],"unavoidable":[90],"confounding":[91,162],"bias":[92],"collected":[95],"data,":[96],"model":[98,150],"directly":[99],"learned":[100],"it":[102],"would":[103,158],"be":[104],"inaccurate.":[105],"One":[106],"well-known":[107],"solution":[108],"Inverse":[110],"Probability":[111],"Weighting":[113],"(IPTW),":[114],"uses":[116],"conditional":[118,146],"probability":[119,216],"given":[122],"covariates":[124,195],"re-weight":[126],"each":[127,154,191],"training":[128,151],"example.":[129],"Applying":[130],"IPTW":[131,175],"however,":[136],"non-trivial.":[138],"We":[139,198,228],"found":[140],"that":[141,180,242],"even":[142],"with":[143,219],"well-estimated":[145],"probability,":[147],"local":[149,189],"step":[152],"using":[153],"hospital's":[155],"alone":[157],"still":[159],"suffer":[160],"bias.":[163],"To":[164],"address":[165],"this,":[166],"propose":[168],"FED-IPTW,":[169],"novel":[171],"algorithm":[172],"extend":[174],"into":[176],"setting":[179],"enforces":[181],"both":[182,232],"global":[183],"(over":[184],"all":[185,248],"data)":[187],"(within":[190],"hospital)":[192],"decorrelation":[193],"between":[194],"treatments.":[197],"validated":[199],"our":[200],"approach":[201],"comparing":[206],"mechanical":[211,267],"ventilation":[212,268],"improving":[214],"survival":[215],"patients":[218],"breadth":[220],"difficulties":[221],"Intensive":[224],"Care":[225],"Unit":[226],"(ICU).":[227],"conducted":[229],"experiments":[230],"synthetic":[233],"eICU":[236],"datasets,":[237],"results":[240],"show":[241],"FED-IPTW":[243],"outperforms":[244],"state-of-the-art":[245],"metrics":[250],"factual":[252],"prediction":[253],"tasks,":[257],"paving":[258],"way":[260],"personalized":[262],"strategy":[264],"design":[265],"usage.":[269]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-01-20T00:00:00"}