  Consider a planner who has limited knowledge of the policy's causal impact on
a certain local population of interest due to a lack of data, but does have
access to the publicized intervention studies performed for similar policies on
different populations. How should the planner make use of and aggregate this
existing evidence to make her policy decision? Following Manski (2020; Towards
Credible Patient-Centered Meta-Analysis, \textit{Epidemiology}), we formulate
the planner's problem as a statistical decision problem with a social welfare
objective, and solve for an optimal aggregation rule under the minimax-regret
criterion. We investigate the analytical properties, computational feasibility,
and welfare regret performance of this rule. We apply the minimax regret
decision rule to two settings: whether to enact an active labor market policy
based on 14 randomized control trial studies; and whether to approve a drug
(Remdesivir) for COVID-19 treatment using a meta-database of clinical trials.
