Swiss Statistics Seminar, Spring 2017
Damian Kozbur (University of Zürich)
This talk describes a post-model selection inference procedure, called 'targeted undersmoothing', designed to construct confidence sets for a broad class of functionals of high-dimensional statistical models. These include dense functionals, which may potentially depend on all elements of an unknown high-dimensional parameter. The proposed confidence sets are based on an initially selected model and two additionally selected models, an upper model and a lower model, which enlarge the initially selected model. The procedure is illustrated with two examples. The first example studies heterogeneous treatment effects in a direct mail marketing campaign, and the second example studies treatment effects of the Job Training Partnership Act of 1982.