{"id":"https://openalex.org/W2964669335","doi":"https://doi.org/10.24963/ijcai.2019/570","title":"On the Estimation of Treatment Effect with Text Covariates","display_name":"On the Estimation of Treatment Effect with Text Covariates","publication_year":2019,"publication_date":"2019-07-28","ids":{"openalex":"https://openalex.org/W2964669335","doi":"https://doi.org/10.24963/ijcai.2019/570","mag":"2964669335"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2019/570","doi":"https://doi.org/10.24963/ijcai.2019/570","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/570","pdf_url":"https://www.ijcai.org/proceedings/2019/0570.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.ijcai.org/proceedings/2019/0570.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032616889","display_name":"Liuyi Yao","orcid":"https://orcid.org/0000-0003-3828-796X","observed_orcids":["https://orcid.org/0000-0003-3828-796X"]},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liuyi Yao","raw_affiliation_strings":["University at Buffalo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100359839","display_name":"Sheng Li","orcid":"https://orcid.org/0000-0003-1205-8632","observed_orcids":["https://orcid.org/0000-0003-1205-8632"]},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sheng Li","raw_affiliation_strings":["University of Georgia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Georgia","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046576694","display_name":"Yaliang Li","orcid":"https://orcid.org/0000-0002-4204-6096","observed_orcids":["https://orcid.org/0000-0002-4204-6096"]},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaliang Li","raw_affiliation_strings":["Alibaba Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alibaba Group","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014632942","display_name":"Hongfei Xue","orcid":"https://orcid.org/0000-0001-9691-9668","observed_orcids":["https://orcid.org/0000-0001-9691-9668"]},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hongfei Xue","raw_affiliation_strings":["University at Buffalo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781385","display_name":"Jing Gao","orcid":"https://orcid.org/0000-0001-5099-6991","observed_orcids":["https://orcid.org/0000-0001-5099-6991","https://orcid.org/0009-0001-1679-439X"]},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Gao","raw_affiliation_strings":["University at Buffalo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013588572","display_name":"Aidong Zhang","orcid":"https://orcid.org/0000-0001-9723-3246","observed_orcids":["https://orcid.org/0000-0001-9723-3246"]},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aidong Zhang","raw_affiliation_strings":["University of Virginia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Virginia","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.0826,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.96088795,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"4106","last_page":"4113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9970999956130981,"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":0.9970999956130981,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.00019999999494757503,"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":9.999999747378752e-05,"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/treatment-effect-estimation","display_name":"treatment effect estimation","score":0.9789999723434448},{"id":"https://openalex.org/keywords/representation-learning","display_name":"representation learning","score":0.7559999823570251},{"id":"https://openalex.org/keywords/estimation-bias","display_name":"estimation bias","score":0.6039999723434448},{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.5329999923706055}],"concepts":[{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.830656111240387},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6629935503005981},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.655048131942749},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.6383928656578064},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6288415193557739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6268891096115112},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.6029468774795532},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5895698070526123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5618318915367126},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5186430811882019},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5181021094322205},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.420957088470459},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33995771408081055},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3375256657600403},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21037030220031738},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2019/570","doi":"https://doi.org/10.24963/ijcai.2019/570","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/570","pdf_url":"https://www.ijcai.org/proceedings/2019/0570.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2019/570","doi":"https://doi.org/10.24963/ijcai.2019/570","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2019/570","pdf_url":"https://www.ijcai.org/proceedings/2019/0570.pdf","source":{"id":"https://openalex.org/S4306419999","display_name":"International Joint Conference on Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4682304715","display_name":"III: Medium: High-Dimensional Interaction Analysis in Bio-Data Sets","funder_award_id":"1514204","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8541794061","display_name":"EAGER: Medical Knowledge Graph Construction from Heterogeneous Sources","funder_award_id":"1747614","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2964669335.pdf","grobid_xml":"https://content.openalex.org/works/W2964669335.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W147186041","https://openalex.org/W1490576214","https://openalex.org/W1574901103","https://openalex.org/W1614298861","https://openalex.org/W1665214252","https://openalex.org/W1973683188","https://openalex.org/W2014188621","https://openalex.org/W2064903582","https://openalex.org/W2111727585","https://openalex.org/W2123394659","https://openalex.org/W2126292488","https://openalex.org/W2132917208","https://openalex.org/W2135046866","https://openalex.org/W2136484149","https://openalex.org/W2144211451","https://openalex.org/W2150291618","https://openalex.org/W2160897769","https://openalex.org/W2162833336","https://openalex.org/W2208550830","https://openalex.org/W2250539671","https://openalex.org/W2263423262","https://openalex.org/W2389937032","https://openalex.org/W2548641835","https://openalex.org/W2562338743","https://openalex.org/W2580460079","https://openalex.org/W2604999042","https://openalex.org/W2605326574","https://openalex.org/W2740222873","https://openalex.org/W2742797692","https://openalex.org/W2781567951","https://openalex.org/W2787244810","https://openalex.org/W2882319491","https://openalex.org/W2890709993","https://openalex.org/W3043997204","https://openalex.org/W3081149207","https://openalex.org/W3121171074","https://openalex.org/W4306855752"],"related_works":["https://openalex.org/W4406277430","https://openalex.org/W4417170765","https://openalex.org/W2752690044","https://openalex.org/W3210609413","https://openalex.org/W4226188747","https://openalex.org/W4385484757","https://openalex.org/W4403786304","https://openalex.org/W4389519020","https://openalex.org/W7130336522","https://openalex.org/W7164136203"],"abstract_inverted_index":{"Estimating":[0],"the":[1,14,54,60,65,68,76,81,100,106,115,123,128,137],"treatment":[2,38,66,77,129,140],"effect":[3,39,78,141],"benefits":[4],"decision":[5],"making":[6],"in":[7],"various":[8],"domains":[9],"as":[10],"it":[11,119],"can":[12],"provide":[13],"potential":[15],"outcomes":[16],"of":[17,139],"different":[18],"choices.":[19],"Existing":[20],"work":[21],"mainly":[22],"focuses":[23],"on":[24,71,150],"covariates":[25,33],"with":[26,34],"numerical":[27],"values,":[28],"while":[29],"how":[30,50],"to":[31,51,64,74,103,109,126],"handle":[32],"textual":[35],"information":[36,107],"for":[37],"estimation":[40,82],"is":[41,49,144],"still":[42],"an":[43],"open":[44],"question.":[45],"One":[46],"major":[47],"challenge":[48],"filter":[52,104],"out":[53,105],"nearly":[55,110],"instrumental":[56,111],"variables":[57,61,73,112],"which":[58,143],"are":[59],"more":[62],"predictive":[63],"than":[67],"outcome.":[69],"Conditioning":[70],"those":[72],"estimate":[75,127],"would":[79],"amplify":[80],"bias.":[83],"To":[84],"address":[85],"this":[86],"challenge,":[87],"we":[88],"propose":[89],"a":[90],"conditional":[91,132],"treatment-adversarial":[92,101,133],"learning":[93,102,114,134],"based":[94],"matching":[95,121],"method":[96],"(CTAM).":[97],"CTAM":[98],"incorporates":[99],"related":[108],"when":[113],"representations,":[116],"and":[117,153],"then":[118],"performs":[120],"among":[122],"learned":[124],"representations":[125],"effects.":[130],"The":[131],"helps":[135],"reduce":[136],"bias":[138],"estimation,":[142],"demonstrated":[145],"by":[146],"our":[147],"experimental":[148],"results":[149],"both":[151],"semi-synthetic":[152],"real-world":[154],"datasets.":[155]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-08-13T00:00:00"}