Maximizing Impact: Using AI for Evaluating Development Effectiveness of IDB Operations and Designing

preparation

To improve lives, the Inter-American Development Bank (IDB) has committed to increasing the impact and scale of our results across Latin America and the Caribbean. Achieving this goal requires a rigorous and sustained effort to evaluate what we do. Impact evaluations are not simply a technical exercise—they are essential for understanding whether our interventions actually lead to better outcomes for people, and how. Without credible evidence of impact, we risk investing in policies or programs that are ineffective or even counterproductive. The value of impact evaluations lies in their ability to generate causal evidence. By isolating the effects of a specific intervention from other confounding factors, these evaluations allow us to determine what works, for whom, and under what conditions. This is particularly important in a region marked by resource constraints and growing demands for transparency. Reliable impact evaluations inform better policy decisions; help allocate resources more efficiently and enhance the legitimacy of development institutions. However, producing robust evaluations requires that operations be designed with evaluation in mind from the outset. This involves identifying a clear theory of change, selecting measurable outcomes, considering appropriate comparison groups, and planning for data collection. Designing for evaluation also means being pragmatic—choosing interventions that allow for sufficient variation or randomization and aligning timelines with evaluation needs.