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Devavrat Shah. Shen, Dennis, Ph. D. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-05-23T16:32:49Z 2018-05-23T16:32:49Z 2018 2018 http://hdl.handle.net/1721.1/115743 1036986794 Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 63-65). In this thesis, we present a robust generalization of the synthetic control method. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, functions without extraneous covariates (vital to existing methods), and overcomes missing data (never been addressed in prior works). To our knowledge, we provide the first theoretical finite sample analysis for a broader class of models than previously considered in literature. Additionally, we relate the inference quality of our estimator to the amount of training data available and show our estimator to be asymptotically consistent. In order to move beyond point estimates, we introduce a Bayesian framework that not only provides practitioners the ability to readily develop different estimators under various loss functions, but also equips them with the tools to quantitatively measure the uncertainty of their model/estimates through posterior probabilities. Our empirical results demonstrate that our robust generalization yields a positive impact over the classical synthetic control method, underscoring the value of our key de-noising procedure. by Dennis Shen. S.M. 88 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Robust synthetic control Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="d91a3c9d-c411-479c-938a-002b18b4cae2"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Robust synthetic control</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Shen, Dennis, Ph. D. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>In this thesis, we present a robust generalization of the synthetic control method. A distinguishing feature of our algorithm is that of de-noising the data matrix via singular value thresholding, which renders our approach robust in multiple facets: it automatically identifies a good subset of donors, functions without extraneous covariates (vital to existing methods), and overcomes missing data (never been addressed in prior works). To our knowledge, we provide the first theoretical finite sample analysis for a broader class of models than previously considered in literature. Additionally, we relate the inference quality of our estimator to the amount of training data available and show our estimator to be asymptotically consistent. In order to move beyond point estimates, we introduce a Bayesian framework that not only provides practitioners the ability to readily develop different estimators under various loss functions, but also equips them with the tools to quantitatively measure the uncertainty of their model/estimates through posterior probabilities. Our empirical results demonstrate that our robust generalization yields a positive impact over the classical synthetic control method, underscoring the value of our key de-noising procedure.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>