Abstract: In this talk we will discuss statistical inference of average treatment effect in measured confounder settings as well as parallel questions of inferring linear and quadratic functionals in generalized linear models under high dimensional proportional asymptotic settings i.e. when pn→δ∈(0,∞) where p,n denote the dimension of the covariates and the sample size respectively . The results rely on the knowledge of the variance covariance matrix of the covariates under study and we show that whereas n-consistent asymptotically normal inference is possible for any by using method of moments type estimators that do not rely on estimating high dimensional nuisance parameters followed by a debiasing strategy. Without the knowledge of we first develop n-consistent estimators by using simple estimators of when <1. Subsequently for ≥1 we develop consistent estimators of the quantities of interest and argue that n-consistent estimation might not be possible without further assumptions on . Finally we verify our results in numerical simulations. This talk is based on joint work with Xingyu Chen and Lin Liu from Shanghai Jiao Tong University.