{"id":"https://openalex.org/W2943820400","doi":"https://doi.org/10.1007/s10618-021-00759-3","title":"Adversarial balancing-based representation learning for causal effect inference with observational data","display_name":"Adversarial balancing-based representation learning for causal effect inference with observational data","publication_year":2021,"publication_date":"2021-05-17","ids":{"openalex":"https://openalex.org/W2943820400","doi":"https://doi.org/10.1007/s10618-021-00759-3","mag":"2943820400"},"language":"en","primary_location":{"id":"doi:10.1007/s10618-021-00759-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00759-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00759-3.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00759-3.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xin Du","orcid":"https://orcid.org/0000-0003-4491-4018"},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Xin Du","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":"https://orcid.org/0000-0003-4491-4018","affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lei Sun","orcid":null},"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":"Lei Sun","raw_affiliation_strings":["University at Buffalo, Buffalo, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo, Buffalo, NY, USA","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wouter Duivesteijn","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Wouter Duivesteijn","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Alexander Nikolaev","orcid":null},"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":"Alexander Nikolaev","raw_affiliation_strings":["University at Buffalo, Buffalo, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo, Buffalo, NY, USA","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"last","author":{"id":null,"display_name":"Mykola Pechenizkiy","orcid":null},"institutions":[{"id":"https://openalex.org/I83019370","display_name":"Eindhoven University of Technology","ror":"https://ror.org/02c2kyt77","country_code":"NL","type":"education","lineage":["https://openalex.org/I83019370"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mykola Pechenizkiy","raw_affiliation_strings":["Eindhoven University of Technology, Eindhoven, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Eindhoven University of Technology, Eindhoven, The Netherlands","institution_ids":["https://openalex.org/I83019370"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I83019370"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":2.9962,"has_fulltext":true,"cited_by_count":23,"citation_normalized_percentile":{"value":0.92362559,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"35","issue":"4","first_page":"1713","last_page":"1738"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.9718999862670898,"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":0.9718999862670898,"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":0.010700000450015068,"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":0.002899999963119626,"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/causal-inference","display_name":"Causal inference","score":0.6847000122070312},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.6705999970436096},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.6610000133514404},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5421000123023987},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5218999981880188},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5113999843597412},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.5031999945640564},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4925999939441681},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.43050000071525574}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.6847000122070312},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.6705999970436096},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.6610000133514404},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.635699987411499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5828999876976013},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5658000111579895},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5421000123023987},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5218999981880188},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5113999843597412},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.43050000071525574},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.3686000108718872},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C155108698","wikidata":"https://www.wikidata.org/wiki/Q1231081","display_name":"Randomized experiment","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.29190000891685486},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C102483320","wikidata":"https://www.wikidata.org/wiki/Q1143725","display_name":"Observational learning","level":3,"score":0.2549000084400177},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2506999969482422},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":7,"locations":[{"id":"doi:10.1007/s10618-021-00759-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00759-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00759-3.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},{"id":"pmh:oai:pure.ed.ac.uk:openaire/d444f215-3022-4b74-b5c0-9204565c1388","is_oa":true,"landing_page_url":"https://www.research.ed.ac.uk/en/publications/d444f215-3022-4b74-b5c0-9204565c1388","pdf_url":null,"source":{"id":"https://openalex.org/S4406922455","display_name":"Edinburgh Research Explorer","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Du, X, Sun, L, Duivesteijn, W, Nikolaev, A & Pechenizkiy, M 2021, 'Adversarial balancing-based representation learning for causal effect inference with observational data', Data Mining and Knowledge Discovery, vol. 35, no. 4, pp. 1713-1738. https://doi.org/10.1007/s10618-021-00759-3","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.tue.nl:openaire/93f85e2e-d2e9-4a6c-a927-75fa55335bf8","is_oa":true,"landing_page_url":"https://research.tue.nl/en/publications/93f85e2e-d2e9-4a6c-a927-75fa55335bf8","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Du, X, Sun, L, Duivesteijn, W, Nikolaev, A & Pechenizkiy, M 2021, 'Adversarial balancing-based representation learning for causal effect inference with observational data', Data Mining and Knowledge Discovery, vol. 35, no. 4, pp. 1713-1738. https://doi.org/10.1007/s10618-021-00759-3","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:arXiv.org:1904.13335","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.13335","pdf_url":"https://arxiv.org/pdf/1904.13335","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pure.tue.nl:publications/fad57938-9866-4c1c-a8a2-c7102e5c4e75","is_oa":true,"landing_page_url":"https://arxiv.org/abs/1904.13335","pdf_url":"https://arxiv.org/pdf/1904.13335","source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Du, X, Sun, L, Duivesteijn, W, Nikolaev, A & Pechenizkiy, M 2019, 'Adversarial balancing-based representation learning for causal effect inference with observational data', arXiv. < https://arxiv.org/abs/1904.13335 >","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pure.tue.nl:openaire_cris_publications/fad57938-9866-4c1c-a8a2-c7102e5c4e75","is_oa":false,"landing_page_url":"https://research.tue.nl/en/publications/fad57938-9866-4c1c-a8a2-c7102e5c4e75","pdf_url":null,"source":{"id":"https://openalex.org/S4406922641","display_name":"TU/e Research Portal","issn_l":null,"issn":null,"is_oa":false,"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":"Du, X, Sun, L, Duivesteijn, W, Nikolaev, A & Pechenizkiy, M 2019, 'Adversarial balancing-based representation learning for causal effect inference with observational data', arXiv. < https://arxiv.org/abs/1904.13335 >","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:tue:oai:pure.tue.nl:publications/93f85e2e-d2e9-4a6c-a927-75fa55335bf8","is_oa":true,"landing_page_url":"https://research.tue.nl/nl/publications/93f85e2e-d2e9-4a6c-a927-75fa55335bf8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Data Mining and Knowledge Discovery, 35(4), 1713 - 1738. Springer Open","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s10618-021-00759-3","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10618-021-00759-3","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10618-021-00759-3.pdf","source":{"id":"https://openalex.org/S121920818","display_name":"Data Mining and Knowledge Discovery","issn_l":"1384-5810","issn":["1384-5810","1573-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Mining and Knowledge Discovery","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2943820400.pdf","grobid_xml":"https://content.openalex.org/works/W2943820400.grobid-xml"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1516659296","https://openalex.org/W1566079370","https://openalex.org/W1995875735","https://openalex.org/W2027739215","https://openalex.org/W2055494407","https://openalex.org/W2064903582","https://openalex.org/W2107072401","https://openalex.org/W2120708938","https://openalex.org/W2132324013","https://openalex.org/W2136484149","https://openalex.org/W2143891888","https://openalex.org/W2150291618","https://openalex.org/W2152456876","https://openalex.org/W2152849583","https://openalex.org/W2162020674","https://openalex.org/W2208550830","https://openalex.org/W2260007524","https://openalex.org/W2396881363","https://openalex.org/W2597503617","https://openalex.org/W2616141209","https://openalex.org/W2770299642","https://openalex.org/W2905402651","https://openalex.org/W2911964244","https://openalex.org/W2945523013","https://openalex.org/W2949999361","https://openalex.org/W3033684741","https://openalex.org/W3121380333","https://openalex.org/W3123974754","https://openalex.org/W3125357717","https://openalex.org/W4232613155","https://openalex.org/W6903588996"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Learning":[1],"causal":[2,79,84,225],"effects":[3,226],"from":[4,60,86,204],"observational":[5,61,87],"data":[6],"greatly":[7],"benefits":[8],"a":[9,28,78,131,231],"variety":[10],"of":[11,27,52,92,106,112,156,167,187,230],"domains":[12],"such":[13],"as":[14],"health":[15,262],"care,":[16],"education,":[17],"and":[18,38,119,172,198,247],"sociology.":[19],"For":[20],"instance,":[21],"one":[22,72],"could":[23],"estimate":[24,82],"the":[25,40,50,54,71,83,90,96,104,107,110,115,120,154,157,165,170,173,177,185,188,195,205,228,249],"impact":[26],"new":[29],"drug":[30],"on":[31,48,70,95,146,184,257],"specific":[32],"individuals":[33],"to":[34,76,81,101,163],"assist":[35],"clinical":[36],"planning":[37],"improve":[39],"survival":[41],"rate.":[42],"In":[43,192],"this":[44,66,215],"paper,":[45],"we":[46,74,99,129],"focus":[47],"studying":[49],"problem":[51,67,218],"estimating":[53],"Conditional":[55],"Average":[56],"Treatment":[57],"Effect":[58,142],"(CATE)":[59],"data.":[62],"The":[63,235],"challenges":[64],"for":[65,140,223],"are":[68,123],"two-fold:":[69],"hand,":[73,98],"have":[75,100],"derive":[77],"estimator":[80],"quantity":[85],"data,":[88],"in":[89,149,169,176],"presence":[91],"confounding":[93],"bias;":[94],"other":[97],"deal":[102],"with":[103],"identification":[105,155],"CATE":[108],"when":[109],"distributions":[111,166],"covariates":[113,168],"over":[114],"treatment":[116,171,189,244],"group":[117,175],"units":[118,122],"control":[121,174],"imbalanced.":[124],"To":[125,152],"overcome":[126],"these":[127],"challenges,":[128],"propose":[130],"neural":[132],"network":[133],"framework":[134],"called":[135],"Adversarial":[136],"Balancing-based":[137],"representation":[138,150,179,196],"learning":[139,162,197],"Causal":[141],"Inference":[143],"(ABCEI),":[144],"based":[145],"recent":[147],"advances":[148],"learning.":[151],"ensure":[153],"CATE,":[158],"ABCEI":[159,212,240],"uses":[160],"adversarial":[161],"balance":[164],"latent":[178],"space,":[180],"without":[181],"any":[182],"assumptions":[183],"form":[186],"selection/assignment":[190],"function.":[191],"addition,":[193],"during":[194],"balancing":[199],"process,":[200],"highly":[201],"predictive":[202],"information":[203,216,222,233],"original":[206],"covariate":[207],"space":[208],"might":[209],"be":[210],"lost.":[211],"can":[213],"tackle":[214],"loss":[217],"by":[219],"preserving":[220],"useful":[221],"predicting":[224],"under":[227],"regularization":[229],"mutual":[232],"estimator.":[234],"experimental":[236],"results":[237,256],"show":[238,254],"that":[239],"is":[241],"robust":[242],"against":[243],"selection":[245],"bias,":[246],"matches/outperforms":[248],"state-of-the-art":[250],"approaches.":[251],"Our":[252],"experiments":[253],"promising":[255],"several":[258,261],"datasets,":[259],"encompassing":[260],"care":[263],"(and":[264],"other)":[265],"domains.":[266]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2019-05-09T00:00:00"}