Brown Statistics Seminar featuring José Zubizarreta, PhD, Harvard University

José R. Zubizaretta, PHD Associate Professor, Department of Health Care Policy, Harvard Medical School Faculty Affiliate, Department of Statistics , Faculty of Arts and Sciences Harvard University “Building Representative Matched Samples with Multi-valued Treatments in Large Observational Studies”  In observational studies of causal effects, matching methods are widely used to approximate the ideal study that would be conducted under controlled experimentation. In this talk, I will discuss new matching methods that use tools from modern optimization to overcome five limitations of standard matching approaches. In particular, these new matching methods (i) directly obtain flexible forms of covariate balance, as specified before matching by the investigator; (ii) produce self-weighting matched samples that are representative of target populations by design; and (iii) handle multiple treatment doses without resorting to a generalization of the propensity score. (iv) These methods can handle large data sets quickly. (v) Unlike standard matching approaches, with these new matching methods, usual estimators are root-n consistent under usual conditions. I will illustrate the performance of these methods in a case study about the impact of a natural disaster on educational opportunity.