<p>In the wake of the credibility revolution, development banks, NGOs, and other economic development and humanitarian agencies are increasingly conducting impact evaluations (also referred as impact assessments) to measure the effectiveness of their programs. Although these institutions have progressively integrated measurement of attributable impact into their results measurement strategies, corporate reporting, and project-level monitoring and evaluation frameworks, a persistent gap remains between evaluative evidence and its application. This paper introduces a novel method for extrapolating treatment effects to quantify and report the number of people benefiting from evaluated programs. The method leverages idiosyncratic (unit-level) treatment effect estimation to enhance the interpretability of distributional impacts and facilitate more actionable and policy relevant project results reporting. Crucially, the method allows analysts to define policy-relevant impact thresholds—such as a minimum income gain—and estimate how many beneficiaries exceeded them. This enables a shift beyond average effects to quantify how many experienced meaningful benefits, and in which subgroups, thereby bridging the gap between rigorous evaluation and real-world decision-making. We demonstrate that this accessible and flexible approach enhances the practical value of evaluations by enabling more nuanced characterization of program impacts across their distribution and scope, moving beyond simple averages.</p>