{"id":"https://openalex.org/W7123552441","doi":"https://doi.org/10.48550/arxiv.2601.06782","title":"Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies","display_name":"Dimension-reduced outcome-weighted learning for estimating individualized treatment regimes in observational studies","publication_year":2026,"publication_date":"2026-01-11","ids":{"openalex":"https://openalex.org/W7123552441","doi":"https://doi.org/10.48550/arxiv.2601.06782"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.06782","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06782","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2601.06782","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113491943","display_name":"Sungtaek Son","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Son, Sungtaek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088099698","display_name":"Eardi Lila","orcid":"https://orcid.org/0000-0002-9199-392X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lila, Eardi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122968126","display_name":"Kwun Chuen Gary Chan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chan, Kwun Chuen Gary","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5113491943"],"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":false,"primary_topic":{"id":"https://openalex.org/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.3930000066757202,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.3930000066757202,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.34150001406669617,"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/T10136","display_name":"Statistical Methods and Inference","score":0.09160000085830688,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.8971999883651733},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.7986999750137329},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5925999879837036},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.5309000015258789},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5127999782562256},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47920000553131104},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.46810001134872437},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.453000009059906},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.3978999853134155},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.38580000400543213}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.8971999883651733},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.7986999750137329},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5925999879837036},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5127999782562256},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49540001153945923},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47920000553131104},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.46810001134872437},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4643999934196472},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.453000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4523000121116638},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38449999690055847},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37400001287460327},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3617999851703644},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.334199994802475},{"id":"https://openalex.org/C27931671","wikidata":"https://www.wikidata.org/wiki/Q7634497","display_name":"Sufficient dimension reduction","level":3,"score":0.33329999446868896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3294000029563904},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C2776376669","wikidata":"https://www.wikidata.org/wiki/Q5094647","display_name":"Intensive care unit","level":2,"score":0.3140999972820282},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C2987370644","wikidata":"https://www.wikidata.org/wiki/Q7836903","display_name":"Treatment effect","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C2987404301","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.26409998536109924},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.06782","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06782","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.06782","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.06782","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":{"Individualized":[0],"treatment":[1,11,55,82,103],"regimes":[2],"(ITRs)":[3],"aim":[4],"to":[5,84,124,150],"improve":[6],"clinical":[7],"outcomes":[8,45],"by":[9],"assigning":[10],"based":[12],"on":[13,86],"patient-specific":[14],"characteristics.":[15],"However,":[16],"existing":[17],"methods":[18],"often":[19],"struggle":[20],"with":[21],"high-dimensional":[22],"covariates,":[23],"limiting":[24],"accuracy,":[25],"interpretability,":[26],"and":[27,46,91,140],"real-world":[28],"applicability.":[29],"We":[30,111,132],"propose":[31],"a":[32,48],"novel":[33],"sufficient":[34,99,107],"dimension":[35],"reduction":[36],"approach":[37],"that":[38,96,113],"directly":[39],"targets":[40],"the":[41,52,87,93,97,114,125],"contrast":[42],"between":[43],"potential":[44],"identifies":[47],"low-dimensional":[49],"subspace":[50,98],"of":[51,65,143],"covariates":[53],"capturing":[54],"effect":[56],"heterogeneity.":[57],"This":[58],"reduced":[59],"representation":[60],"enables":[61],"more":[62],"accurate":[63],"estimation":[64],"optimal":[66],"ITRs":[67],"through":[68,138],"outcome-weighted":[69],"learning.":[70],"To":[71],"accommodate":[72],"observational":[73],"data,":[74],"our":[75],"method":[76,116],"incorporates":[77],"kernel-based":[78],"covariate":[79,89],"balancing,":[80],"allowing":[81],"assignment":[83],"depend":[85],"full":[88],"set":[90],"avoiding":[92],"restrictive":[94],"assumption":[95],"for":[100,108],"modeling":[101],"heterogeneous":[102],"effects":[104],"is":[105],"also":[106],"confounding":[109],"adjustment.":[110],"show":[112],"proposed":[115],"achieves":[117],"universal":[118],"consistency,":[119],"i.e.,":[120],"its":[121,134],"risk":[122],"converges":[123],"Bayes":[126],"risk,":[127],"under":[128],"mild":[129],"regularity":[130],"conditions.":[131],"demonstrate":[133],"finite":[135],"sample":[136],"performance":[137],"simulations":[139],"an":[141],"analysis":[142],"intensive":[144],"care":[145],"unit":[146],"sepsis":[147],"patient":[148],"data":[149],"determine":[151],"who":[152],"should":[153],"receive":[154],"transthoracic":[155],"echocardiography.":[156]},"counts_by_year":[],"updated_date":"2026-05-03T08:25:01.440150","created_date":"2026-01-14T00:00:00"}