Seminar by Dr. Zijian GUO from Rutgers University

Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding. We focus on a high-dimensional linear regression setting, where the measured covariates are affected by hidden confounding and propose the Doubly Debiased Lasso estimator for individual components of the regression coefficient vector. Our advocated method simultaneously corrects both the bias due to estimation of high-dimensional parameters as well as the bias caused by the hidden confounding. We establish its asymptotic normality and also prove that it is efficient in the Gauss-Markov sense. The validity of our methodology relies on a dense confounding assumption, i.e. that every confounding variable affects many covariates. The finite sample performance is illustrated with an extensive simulation study and a genomic application. This is based on a joint work with Domagoj Cevid and Peter Bühlmann. The paper draft can be found at https://arxiv.org/abs/2004.03758

Dr. Zijian Guo is an Assistant Professor in Department of Statistics, at Rutgers University. He received his Ph.D. in Statistics at University of Pennsylvania in 2017 advised by Professor T. Tony Cai. Before that, He received bachelor degree in Mathematics from The Chinese University of Hong Kong.