4/20/2017 | 4:15pm | E51-335
Reception to follow.
Abstract: In observational studies of causal effects, matching methods are often used to approximate the ideal study that would be conducted if it were possible to do it by controlled experimentation. In this talk, I will discuss new matching methods based on mathematical programming that allow the investigator to overcome three limitations of standard matching approaches by: (i) directly obtaining flexible forms of covariate balance; (ii) producing self-weighting matched samples that are representative by design; and (iii) handling multiple treatment doses without resorting to a generalization of the propensity score. (iv) Unlike standard matching approaches, with these new matching methods typical estimators are root-n consistent under the usual conditions. I will illustrate the performance of these methods in real and simulated data sets.
This is joint work with Magdalena Bennett, David Hirshberg and Juan Pablo Vielma.
Bio: Jose Zubizarreta is Assistant Professor at the Division of Decision, Risk, and Operations, and the Department of Statistics at Columbia University. His research focuses on statistical methods for causal inference and impact evaluation, with applications to the health and social sciences. Among others, his methodological work has been published in the Journal of the American Statistical Association, the Annals of Applied Statistics, and Biometrika. His substantive work has appeared in the Journal of the American Medical Association, the Annals of Surgery, and the Journal of Perinatology. Among other awards, he received the Kenneth Rothman Award for the best publication in Epidemiology in 2013 and his research has been funded by the Alfred P. Sloan Foundation.