Title: Regularized calibrated estimation and model-assisted inference for treatment effects with high-dimensional data
Authors: Zhiqiang Tan - Rutgers University (United States) [presenting]
Abstract: Consider the problem of estimating average treatment effects in the framework of potential outcomes when a large number of covariates are used to adjust for possible confounding through outcome regression and propensity score models. We develop new methods and theory to obtain doubly robust point estimators for average treatment effects, which remain consistent if either the propensity score model or the outcome regression model is correctly specified. We also obtain model-assisted confidence intervals, which are valid when the propensity score model is correctly specified, but the outcome regression model may be misspecified. Our methods involve regularized calibrated estimators with Lasso penalties, but carefully chosen loss functions, for fitting propensity score and outcome regression models. We provide high-dimensional analysis to establish the desired properties of our methods under comparable sparsity conditions to previous results, which give valid confidence intervals when both the propensity score and outcome models are correctly specified. We present simulation studies and an empirical application which demonstrate advantages of the proposed methods compared with related methods based on regularized maximum likelihood estimation.