Generalizing Results from Randomized Trials to a Target Population via Weighting Methods (303219)

Keywords: Causal inference, External validity, Generalizability, Randomized trials, Propensity scores

Randomized controlled trials (RCTs) provide strong internal validity compared with observational studies. However, selection bias threatens the external validity of randomized trials. Thus, RCT results may not apply to either broad public policy populations or narrow populations, such as specific insurance pools. Some researchers use propensity scores (PSs) to generalize results from an RCT to a target population. In this scenario, a PS is defined as the probability of participating in the trial conditioning on observed covariates. We study a model-free inverse probability weighted (IPW) estimator of the average treatment effect in a target population with data from a randomized trial. We estimate PSs in two ways, estimating them together (treatment and control groups in trials combined) and separately (treatment and control groups modeled separately). We present variance estimators and compare the performance of our method with that of model-based approaches. We examine the robustness of the model-free estimators to heterogeneous treatment effects.