Title: Building Representative Matched Samples in Large-Scale Observational Studies with Multivalued Treatments

Abstract:  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 four 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. I will illustrate the performance of these methods in a case studies about the impact of an earthquake on post-traumatic stress and standardized test scores.