  The ex-ante evaluation of policies using structural econometric models is
based on estimated parameters as a stand-in for the true parameters. This
practice ignores uncertainty in the counterfactual policy predictions of the
model. We develop a generic approach that deals with parametric uncertainty
using uncertainty sets and frames model-informed policy-making as a decision
problem under uncertainty. The seminal human capital investment model by Keane
and Wolpin (1997) provides a well-known, influential, and empirically-grounded
test case. We document considerable uncertainty in the models's policy
predictions and highlight the resulting policy recommendations obtained from
using different formal rules of decision-making under uncertainty.
