{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/92872"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/92872","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Applications of balance optimization subset selection","abstract":"Balance Optimization Subset Selection (BOSS) is a framework designed to be used for causal inference on observational data. The theoretical foundation for the BOSS framework has been provided in the literature; this thesis aims to provide some examples of the practical value of BOSS by using it on two problems. The first application is using BOSS to determine a subset of users who would be suitable targets for marketing efforts, and the second application is using BOSS to identify potential first-round upsets in the NCAA basketball tournament. 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