Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data

Understand

Consider the problem of estimating average treatment effects when a large number of covariates are used to adjust for possible confounding through outcome regression and propensity score models.

• The conventional approach of model building and fitting iteratively can be difficult to implement, depending on ad hoc choices of what variables are included.
• In addition, uncertainty from the iterative process of model selection is complicated and often ignored in subsequent inference about treatment effects.
• We develop new methods and theory to obtain not only 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, but also model-assisted confidence intervals, which are valid when the propensity score model is correctly specified but the outcome regression model may be misspecified.