{"id":"https://openalex.org/W7155070759","doi":"https://doi.org/10.48550/arxiv.2604.16763","title":"LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies","display_name":"LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies","publication_year":2026,"publication_date":"2026-04-18","ids":{"openalex":"https://openalex.org/W7155070759","doi":"https://doi.org/10.48550/arxiv.2604.16763"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.16763","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16763","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.16763","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134130394","display_name":"Lei Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134169310","display_name":"Jialin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jialin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070011635","display_name":"Kathy Macropol","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Macropol, Kathy","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":false,"primary_topic":{"id":"https://openalex.org/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.39089998602867126,"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":0.39089998602867126,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.3540000021457672,"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":0.043699998408555984,"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":0.8916000127792358},{"id":"https://openalex.org/keywords/propensity-score-matching","display_name":"Propensity score matching","score":0.8492000102996826},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.7315000295639038},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6517999768257141},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5019000172615051},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.4961000084877014},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4878000020980835},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4713999927043915}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.8916000127792358},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.8492000102996826},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.7315000295639038},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6517999768257141},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5867000222206116},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5019000172615051},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.4961000084877014},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4878000020980835},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4713999927043915},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.46700000762939453},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.46320000290870667},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44839999079704285},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.44119998812675476},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4122999906539917},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.37959998846054077},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3490999937057495},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3190999925136566},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.27639999985694885},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.27619999647140503},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.16763","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16763","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.16763","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.16763","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Causal":[0],"inference":[1],"from":[2,32,148,176],"electronic":[3],"health":[4],"records":[5],"(EHR)":[6],"is":[7,99],"fundamentally":[8],"limited":[9],"by":[10],"unmeasured":[11],"confounding:":[12],"critical":[13,215],"clinical":[14],"states":[15],"such":[16],"as":[17,43],"frailty,":[18],"goals":[19],"of":[20,79,197],"care,":[21],"and":[22,92,132,156,187,207],"mental":[23],"status":[24],"are":[25,105],"documented":[26],"in":[27,214],"free-text":[28],"notes":[29],"but":[30],"absent":[31],"structured":[33,45],"data.":[34],"Large":[35],"language":[36],"models":[37],"can":[38],"extract":[39],"these":[40],"latent":[41],"confounders":[42],"interpretable,":[44],"covariates,":[46],"yet":[47],"how":[48,208],"to":[49,150,153,178],"effectively":[50],"integrate":[51],"them":[52],"into":[53],"causal":[54,212],"estimation":[55,146,213],"pipelines":[56],"has":[57],"not":[58,101],"been":[59],"systematically":[60],"studied.":[61],"Using":[62],"the":[63,77,110,118,129,172,183,195],"MIMIC-IV":[64],"database":[65],"with":[66,114,138,182],"21,859":[67],"sepsis":[68],"patients,":[69],"we":[70],"compare":[71],"seven":[72],"covariate-integration":[73],"strategies":[74,104],"for":[75],"estimating":[76],"effect":[78,175],"early":[80],"vasopressor":[81],"initiation":[82],"on":[83,124,205],"28-day":[84],"mortality,":[85],"spanning":[86],"tabular-only":[87,154],"baselines,":[88],"traditional":[89],"NLP":[90],"representations,":[91],"three":[93],"LLM-augmented":[94,142],"approaches.":[95],"A":[96],"central":[97],"finding":[98],"that":[100],"all":[102],"integration":[103],"equally":[106],"effective:":[107],"directly":[108],"augmenting":[109],"propensity":[111,143],"score":[112],"model":[113],"LLM":[115],"covariates":[116,170,210],"achieves":[117],"best":[119],"performance,":[120],"while":[121],"dual-caliper":[122],"matching":[123],"text-derived":[125,209],"categorical":[126],"distances":[127],"restricts":[128],"donor":[130],"pool":[131],"degrades":[133],"estimation.":[134],"In":[135],"semi-synthetic":[136],"experiments":[137],"known":[139],"ground-truth":[140],"effects,":[141],"scores":[144],"reduce":[145],"bias":[147],"0.0143":[149],"0.0003":[151],"relative":[152],"methods,":[155],"this":[157,198],"advantage":[158],"persists":[159],"under":[160],"substantial":[161],"simulated":[162],"extraction":[163],"error.":[164],"On":[165],"real":[166],"data,":[167],"incorporating":[168],"LLM-extracted":[169],"reduces":[171],"estimated":[173],"treatment":[174],"0.055":[177],"0.027,":[179],"directionally":[180],"consistent":[181],"CLOVERS":[184],"randomized":[185],"trial,":[186],"a":[188],"doubly":[189],"robust":[190],"estimator":[191],"yielding":[192],"0.019":[193],"confirms":[194],"robustness":[196],"finding.":[199],"Our":[200],"results":[201],"offer":[202],"practical":[203],"guidance":[204],"when":[206],"improve":[211],"care.":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-22T00:00:00"}
