Despite recent advances in methods and technology, collecting data on population health and inequality remains financially and logistically taxing, particularly in low and lower-middle income countries. In such settings, policymakers in areas with sparse data rely on estimates produced using models that extrapolate trends from other regions or time periods. This talk introduces a framework for decision-making that incorporates uncertainty about the quality of these extrapolations. Drawing on work in economics, we use a utility function with ambiguity aversion, a preference for known risks. Using this setup, we construct interpretable decision-rules in several common policy settings. We demonstrate our approach in the context of a decision about resource allocation based on healthcare facilities in India.