Leveraging population outcomes to improve the generalization of experimental results: Application to the JTPA study

Generalizing causal estimates in randomized experiments to a broader target population is essential for guiding decisions by policymakers and practitioners in the social and biomedical sciences. While recent papers developed various weighting estimators for the population average treatment effect (PATE), many of these methods result in large variance because the experimental sample often differs substantially from the target population, and estimated sampling weights are extreme. We investigate this practical problem motivated by an evaluation study of the Job Training Partnership Act (JTPA), where we examine how well we can generalize the causal effect of job training programs beyond a specific population of economically disadvantaged adults and youths. In particular, we propose post-residualized weighting in which we use the outcome measured in the observational population data to build a flexible predictive model (e.g., machine learning methods) and residualize the outcome in the experimental data before using conventional weighting methods. We show that the proposed PATE estimator is consistent under the same assumptions required for existing weighting methods, impor-tantly without assuming the correct specification of the predictive model. We demonstrate the efficiency gains from this approach through our JTPA application: we find a between 5 and 25% reduction in variance.