Externally Valid Policy Evaluation Combining Trial and Observational Data
This addresses the issue of generalizing trial results to broader populations for policymakers and researchers, representing an incremental improvement in methodology.
The paper tackles the problem of external validity in policy evaluation by combining trial and observational data to draw valid inferences about policy outcomes on a target population, ensuring certifiably valid evaluations under model miscalibrations with nonparametric methods and finite-sample guarantees.
Randomized trials are widely considered as the gold standard for evaluating the effects of decision policies. Trial data is, however, drawn from a population which may differ from the intended target population and this raises a problem of external validity (aka. generalizability). In this paper we seek to use trial data to draw valid inferences about the outcome of a policy on the target population. Additional covariate data from the target population is used to model the sampling of individuals in the trial study. We develop a method that yields certifiably valid trial-based policy evaluations under any specified range of model miscalibrations. The method is nonparametric and the validity is assured even with finite samples. The certified policy evaluations are illustrated using both simulated and real data.