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Generalizing causal estimates in randomized experiments to a broader targetpopulation is essential for guiding decisions by policymakers and practitionersin the social and biomedical sciences. While recent papers developed variousweighting estimators for the population average treatment effect (PATE), manyof these methods result in large variance because the experimental sample oftendiffers substantially from the target population, and estimated samplingweights are extreme. To improve efficiency in practice, we proposepost-residualized weighting in which we use the outcome measured in theobservational population data to build a flexible predictive model (e.g.,machine learning methods) and residualize the outcome in the experimental databefore using conventional weighting methods. We show that the proposed PATEestimator is consistent under the same assumptions required for existingweighting methods, importantly without assuming the correct specification ofthe predictive model. We demonstrate the efficiency gains from this approachthrough simulations and our application based on a set of job trainingexperiments.

Leveraging Population Outcomes to Improve the Generalization of Experimental Results