{"id":"https://openalex.org/W4417142438","doi":"https://doi.org/10.48550/arxiv.2512.05373","title":"Text Rationalization for Robust Causal Effect Estimation","display_name":"Text Rationalization for Robust Causal Effect Estimation","publication_year":2025,"publication_date":"2025-12-05","ids":{"openalex":"https://openalex.org/W4417142438","doi":"https://doi.org/10.48550/arxiv.2512.05373"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.05373","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.05373","pdf_url":"https://arxiv.org/pdf/2512.05373","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.05373","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018637521","display_name":"L.H. Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhang, Lijinghua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5072907251","display_name":"Hengrui Cai","orcid":"https://orcid.org/0000-0002-5679-8862"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Hengrui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5018637521"],"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.7767000198364258,"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"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.7767000198364258,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.07050000131130219,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.02800000086426735,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.8202999830245972},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.6460999846458435},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.5145000219345093},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.4629000127315521},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.40689998865127563},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3930000066757202},{"id":"https://openalex.org/keywords/rationalization","display_name":"Rationalization (economics)","score":0.3785000145435333},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3560999929904938}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.8202999830245972},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6460999846458435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6115000247955322},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.5145000219345093},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.4629000127315521},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4262999892234802},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3930000066757202},{"id":"https://openalex.org/C52438962","wikidata":"https://www.wikidata.org/wiki/Q1555139","display_name":"Rationalization (economics)","level":2,"score":0.3785000145435333},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37619999051094055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3564999997615814},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.34310001134872437},{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.33899998664855957},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2881999909877777},{"id":"https://openalex.org/C79897977","wikidata":"https://www.wikidata.org/wiki/Q5054568","display_name":"Causal chain","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2711000144481659},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.25540000200271606}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.05373","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.05373","pdf_url":"https://arxiv.org/pdf/2512.05373","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"text"},{"id":"doi:10.48550/arxiv.2512.05373","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.05373","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":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.05373","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.05373","pdf_url":"https://arxiv.org/pdf/2512.05373","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"text"},"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":{"Recent":[0],"advances":[1],"in":[2,14,22,69,88],"natural":[3],"language":[4],"processing":[5],"have":[6],"enabled":[7],"the":[8,46,61,150],"increasing":[9],"use":[10],"of":[11,108],"text":[12,28,66],"data":[13,144],"causal":[15,40,138,162],"inference,":[16],"particularly":[17],"for":[18,39,120],"adjusting":[19],"confounding":[20,117],"factors":[21],"treatment":[23,52],"effect":[24,89,139,163],"estimation.":[25,43],"Although":[26],"high-dimensional":[27],"can":[29],"encode":[30],"rich":[31],"contextual":[32],"information,":[33],"it":[34],"also":[35],"poses":[36],"unique":[37],"challenges":[38,94],"identification":[41],"and":[42,85,135,145,160],"In":[44],"particular,":[45],"positivity":[47,133],"assumption,":[48],"which":[49],"requires":[50],"sufficient":[51,119],"overlap":[53],"across":[54],"confounder":[55],"values,":[56],"is":[57,67],"often":[58],"violated":[59],"at":[60],"observational":[62],"level,":[63],"when":[64],"massive":[65],"represented":[68],"feature":[70],"spaces.":[71],"Redundant":[72],"or":[73],"spurious":[74],"textual":[75],"features":[76],"inflate":[77],"dimensionality,":[78],"producing":[79],"extreme":[80],"propensity":[81],"scores,":[82],"unstable":[83],"weights,":[84],"inflated":[86],"variance":[87],"estimates.":[90],"We":[91],"address":[92],"these":[93],"with":[95],"Confounding-Aware":[96],"Token":[97],"Rationalization":[98],"(CATR),":[99],"a":[100,104,111,146],"framework":[101],"that":[102,154],"selects":[103],"sparse":[105],"necessary":[106],"subset":[107],"tokens":[109],"using":[110,149],"residual-independence":[112],"diagnostic":[113],"designed":[114],"to":[115],"preserve":[116],"information":[118],"unconfoundedness.":[121],"By":[122],"discarding":[123],"irrelevant":[124],"texts":[125],"while":[126],"retaining":[127],"key":[128],"signals,":[129],"CATR":[130,155],"mitigates":[131],"observational-level":[132],"violations":[134],"stabilizes":[136],"downstream":[137],"estimators.":[140],"Experiments":[141],"on":[142],"synthetic":[143],"real-world":[147],"study":[148],"MIMIC-III":[151],"database":[152],"demonstrate":[153],"yields":[156],"more":[157],"accurate,":[158],"stable,":[159],"interpretable":[161],"estimates":[164],"than":[165],"existing":[166],"baselines.":[167]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-09T00:00:00"}
