{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/100968"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/100968","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"New developments in causal inference using balance optimization subset selection","abstract":"Causal inference with observational data has drawn attention across various fields. These observational studies typically use matching methods which find matched pairs with similar covariate values. However, matching methods may not directly achieve covariate balance, a measure of matching effectiveness. As an alternative, the Balance Optimization Subset Selection (BOSS) framework, which seeks the optimal covariate balance directly, has been proposed. This dissertation extends the BOSS framework in various ways and is composed of the following five parts. The first part of the dissertation investigates all the possible cases that may lead to bias in the context of BOSS and tries to mitigate the bias. Second, this dissertation then extends the BOSS by estimating and decomposing a treatment effect as a combination of heterogeneous treatment effects from a partitioned set using the BOSS. Third, the dissertation generalizes the BOSS framework from a binary treatment setting to a multi-treatment setting. A treatment effect estimate with multiple treatments can be computed by combining estimates obtained from BOSS with binary treatments. The fourth part discusses on how to handle missing data with BOSS. It includes a sensitivity analysis of BOSS studying how the estimated values are affected by violation of the conditional independence assumption and methods to apply BOSS after multiple imputation on missing covariates. In these discussions, the performances of BOSS estimators are compared to those of matching estimators. In the last part, BOSS is formulated as an LP by relaxing integer constraints in the original mixed integer programming formulation and properties of its dual problem are investigated.","abstract_html":"Causal inference with observational data has drawn attention across various fields. These observational studies typically use matching methods which find matched pairs with similar covariate values. However, matching methods may not directly achieve covariate balance, a measure of matching effectiveness. As an alternative, the Balance Optimization Subset Selection (BOSS) framework, which seeks the optimal covariate balance directly, has been proposed. This dissertation extends the BOSS framework in various ways and is composed of the following five parts. The first part of the dissertation investigates all the possible cases that may lead to bias in the context of BOSS and tries to mitigate the bias. Second, this dissertation then extends the BOSS by estimating and decomposing a treatment effect as a combination of heterogeneous treatment effects from a partitioned set using the BOSS. Third, the dissertation generalizes the BOSS framework from a binary treatment setting to a multi-treatment setting. A treatment effect estimate with multiple treatments can be computed by combining estimates obtained from BOSS with binary treatments. The fourth part discusses on how to handle missing data with BOSS. It includes a sensitivity analysis of BOSS studying how the estimated values are affected by violation of the conditional independence assumption and methods to apply BOSS after multiple imputation on missing covariates. In these discussions, the performances of BOSS estimators are compared to those of matching estimators. In the last part, BOSS is formulated as an LP by relaxing integer constraints in the original mixed integer programming formulation and properties of its dual problem are investigated.","abstract_has_math":false,"creators":["Kwon, Hee Youn"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Systems & Entrepreneurial Engr","degree_department":null,"school":null,"contributors":["Jacobson, Sheldon H.","He, Niao","Chandrasekaran, Karthekeyan","Nagi, Rakesh"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05","date_published":"2018-05","updated_at":"2026-07-22T22:24:38Z","subjects":["Causal Analysis","Optimization","Subset Selection"],"languages":["en"],"rights":["Copyright 2018 Hee Youn Kwon"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/100968","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jacobson, Sheldon H.","He, Niao","Chandrasekaran, Karthekeyan","Nagi, Rakesh"]},{"key":"dc:creator","label":"Author","values":["Kwon, Hee Youn"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-05","2018-09-04T20:27:03Z","2018-04-13"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Systems & Entrepreneurial Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Causal Analysis","Optimization","Subset Selection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Hee Youn Kwon"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/100968"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Causal inference with observational data has drawn attention across various fields. These observational studies typically use matching methods which find matched pairs with similar covariate values. However, matching methods may not directly achieve covariate balance, a measure of matching effectiveness. As an alternative, the Balance Optimization Subset Selection (BOSS) framework, which seeks the optimal covariate balance directly, has been proposed. This dissertation extends the BOSS framework in various ways and is composed of the following five parts. The first part of the dissertation investigates all the possible cases that may lead to bias in the context of BOSS and tries to mitigate the bias. Second, this dissertation then extends the BOSS by estimating and decomposing a treatment effect as a combination of heterogeneous treatment effects from a partitioned set using the BOSS. Third, the dissertation generalizes the BOSS framework from a binary treatment setting to a multi-treatment setting. A treatment effect estimate with multiple treatments can be computed by combining estimates obtained from BOSS with binary treatments. The fourth part discusses on how to handle missing data with BOSS. It includes a sensitivity analysis of BOSS studying how the estimated values are affected by violation of the conditional independence assumption and methods to apply BOSS after multiple imputation on missing covariates. In these discussions, the performances of BOSS estimators are compared to those of matching estimators. In the last part, BOSS is formulated as an LP by relaxing integer constraints in the original mixed integer programming formulation and properties of its dual problem are investigated.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Hee Youn Kwon, accepted the attached license on 2018-04-13 at 08:52.","The student, Hee Youn Kwon, submitted this Dissertation for approval on 2018-04-13 at 08:57.","This Dissertation was approved for publication on 2018-04-13 at 11:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12228 on 2018-08-31 at 17:12:14","Made available in DSpace on 2018-09-04T20:27:03Z (GMT). No. of bitstreams: 3 KWON-DISSERTATION-2018.pdf: 616872 bytes, checksum: 9c51ad3a290440c100bbed1f31ee78b9 (MD5) LICENSE.txt: 4210 bytes, checksum: a73fcbe17e658ba0a5178b3889a1f09c (MD5) PROQUEST_LICENSE.txt: 4556 bytes, checksum: f26de9c06d77b66ae3c1721cbf96cdbc (MD5) Previous issue date: 2018-04-13"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["New developments in causal inference using balance optimization subset selection"]}]}],"canonical_facts":{"dc:contributor":["Jacobson, Sheldon H.","He, Niao","Chandrasekaran, Karthekeyan","Nagi, Rakesh"],"dc:creator":["Kwon, Hee Youn"],"dc:date":["2018-05","2018-09-04T20:27:03Z","2018-04-13"],"dc:description":["Causal inference with observational data has drawn attention across various fields. These observational studies typically use matching methods which find matched pairs with similar covariate values. However, matching methods may not directly achieve covariate balance, a measure of matching effectiveness. As an alternative, the Balance Optimization Subset Selection (BOSS) framework, which seeks the optimal covariate balance directly, has been proposed. This dissertation extends the BOSS framework in various ways and is composed of the following five parts. The first part of the dissertation investigates all the possible cases that may lead to bias in the context of BOSS and tries to mitigate the bias. Second, this dissertation then extends the BOSS by estimating and decomposing a treatment effect as a combination of heterogeneous treatment effects from a partitioned set using the BOSS. Third, the dissertation generalizes the BOSS framework from a binary treatment setting to a multi-treatment setting. A treatment effect estimate with multiple treatments can be computed by combining estimates obtained from BOSS with binary treatments. The fourth part discusses on how to handle missing data with BOSS. It includes a sensitivity analysis of BOSS studying how the estimated values are affected by violation of the conditional independence assumption and methods to apply BOSS after multiple imputation on missing covariates. In these discussions, the performances of BOSS estimators are compared to those of matching estimators. In the last part, BOSS is formulated as an LP by relaxing integer constraints in the original mixed integer programming formulation and properties of its dual problem are investigated.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Hee Youn Kwon, accepted the attached license on 2018-04-13 at 08:52.","The student, Hee Youn Kwon, submitted this Dissertation for approval on 2018-04-13 at 08:57.","This Dissertation was approved for publication on 2018-04-13 at 11:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12228 on 2018-08-31 at 17:12:14","Made available in DSpace on 2018-09-04T20:27:03Z (GMT). No. of bitstreams: 3 KWON-DISSERTATION-2018.pdf: 616872 bytes, checksum: 9c51ad3a290440c100bbed1f31ee78b9 (MD5) LICENSE.txt: 4210 bytes, checksum: a73fcbe17e658ba0a5178b3889a1f09c (MD5) PROQUEST_LICENSE.txt: 4556 bytes, checksum: f26de9c06d77b66ae3c1721cbf96cdbc (MD5) Previous issue date: 2018-04-13"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/100968"],"dc:language":["en"],"dc:rights":["Copyright 2018 Hee Youn Kwon"],"dc:subject":["Causal Analysis","Optimization","Subset Selection"],"dc:title":["New developments in causal inference using balance optimization subset selection"],"dc:type":["text"],"thesis:degree_discipline":["Systems & Entrepreneurial Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:38Z"}