Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data

Sofia Ek, Dave Zachariah

Keywords: Policy Evaluation, Randomized Trials, External Validity

TL;DR: We propose a nonparametric method that uses trial data, adjusted with additional covariates from the target population, to provide certifiably and externally valid policy evaluations.

Abstract: 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.

Primary Area: Machine learning for healthcare

Submission Number: 9094