Appropriate causal inference methods are required for comparative effectiveness research to produce valid and relevant findings from observational data. Two prominent classes of such methods are based on unconfoundedness or instrumental variable (IV) assumptions. Although extensive research has been done, it remains highly challenging to estimate propensity scores (PSs) and regression functions and to perform subsequent inference about average treatment effects (ATEs). The conventional approach employs an iterative process of model building and fitting, depending on ad hoc modeling choices, where statistical uncertainty is difficult to quantify. Recently, various methods have been proposed that apply off-the-shelf machine learning algorithms but either ignore statistical inference or invoke strong smoothness assumptions to justify consistent estimation of regression functions and subsequent statistical inference about treatment effects.
