Rigorous quantitative and qualitative approaches to evaluate the effectiveness of interventions and provide evidence-based insights for policy making.
While RCTs provide high causal rigor, Quasi-experimental methods (DiD, RDD) are more common in "real-world" settings where we evaluate policies after they have already begun. For a framework to be effective, it must match the Theory of Change developed at the start of the project.
The "Gold Standard" of impact evaluation, using random assignment to eliminate selection bias and isolate the true effect of a program.
Uses "Parallel Paths" logic to compare the change in outcomes over time between a treatment group and a comparison group.
Evaluates program impact by comparing individuals just above and just below a strict numeric eligibility cutoff.
Statistically matches participants with non-participants who share similar observable traits to "create" a control group.
Uses an external "Instrument" factor that affects participation but not the outcome directly to account for unobserved bias.
The blueprint for impact. Unlike mathematical methods, this is a conceptual map used during the planning phase to link activities to long-term goals.
A Theory of Change defines the "logic" of why a project will work. It maps out the causal link between what we do and what we hope to achieve.
Resources needed (funding, staff, tech).
Actions taken (building, training, advising).
Immediate results (wells built, students trained).
Short-term changes (better health, higher scores).
Long-term goal (lower mortality, economic growth).
We help you choose the right framework for your specific project needs.
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