Victor Chernozhukov: Inference on treatment effects after selection amongst high-dimensional controls
Victor Chernozhukov · Jan 18, 2013
Date: 2013-01-18 Time: 14:30-15:30 Location: BURN 306 Abstract: We propose robust methods for inference on the effect of a treatment variable on a scalar outcome in the presence of very many controls. Our setting is a partially linear model with possibly non-Gaussian and heteroscedastic disturbances. Our analysis allows the number of controls to be much larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlled for up to a small approximation error by conditioning on a relatively small number of controls whose identities are unknown.