The process of choosing a hypothetical scenario (“counterfactual”), a core principle of impact evaluation, can be technically difficult, data-intensive and subject to significant uncertainties (see e.g. Clapp and Prag, 2012). Without meaningful baselines or counterfactuals, however, it is difficult to assess the causal relationship between the observed impacts and the goals of a specific intervention. This is especially relevant for indicators used to compare interventions or that are aggregated for tracking and reporting at larger scales, since data collected using dissimilar or vague indicators may not be comparable.