Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data
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.
Cite
Text
Ek and Zachariah. "Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data." Neural Information Processing Systems, 2024. doi:10.52202/079017-2230Markdown
[Ek and Zachariah. "Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/ek2024neurips-externally/) doi:10.52202/079017-2230BibTeX
@inproceedings{ek2024neurips-externally,
title = {{Externally Valid Policy Evaluation from Randomized Trials Using Additional Observational Data}},
author = {Ek, Sofia and Zachariah, Dave},
booktitle = {Neural Information Processing Systems},
year = {2024},
doi = {10.52202/079017-2230},
url = {https://mlanthology.org/neurips/2024/ek2024neurips-externally/}
}