Did it change because of you — or would it have changed anyway?
Impact assessment isolates the change your intervention actually caused, using a credible counterfactual, and measures whether it lasted after the funding stopped.
Attribution is the hardest claim in evaluation, and the most valuable
Incomes rose 18%. Excellent — but the harvest was also good, a road was tarred and a second organisation ran a similar programme nearby. How much of that 18% was you?
Impact assessment answers that with a counterfactual: what would have happened without the intervention. Depending on what the design allows, we use comparison groups, difference-in-differences, propensity score matching or rigorous contribution analysis — and we state honestly how strong the resulting claim is.
How we run it
Design the counterfactual
Establish the strongest available comparison given the programme design and existing data.
Collect from both groups
Survey participants and comparison population using instruments comparable to baseline.
Estimate the effect
Statistical estimation with controls, robustness checks and confidence intervals reported.
Assess sustainability
Whether change persisted after support ended, and what maintains or erodes it.
What you receive
Attributable impact estimate
The measured effect with confidence intervals and a plain statement of attribution strength.
Counterfactual analysis
The comparison basis, why it is credible, and what threatens its validity.
Unintended consequences
Effects nobody planned for — positive and negative, on participants and non-participants.
Cost-effectiveness
Cost per unit of outcome achieved, comparable to alternative interventions where benchmarks exist.
Sustainability findings
Whether the change held after funding ended, and the mechanisms keeping it in place.
Strategic implications
What to scale, what to redesign and what not to repeat, with the reasoning shown.
How we establish the counterfactual
Comparison group design
A similar population that did not receive the intervention, matched on observable characteristics.
Difference-in-differences
Change in the treated group compared to change in the comparison group over the same period.
Propensity score matching
Statistical matching where participants self-selected rather than being randomly assigned.
Contribution analysis
Where no counterfactual is possible, a rigorous causal narrative testing rival explanations.
Often commissioned alongside this
Baseline Evaluations
Establish a defensible benchmark before implementation
OpenMidline Evaluations
Course-correct while there is still time to act
OpenEndline Evaluations
Prove outcomes, effectiveness and value for money
OpenProcess Evaluations
Diagnose delivery quality and operational efficiency
OpenNeeds Assessments
Locate the real gaps before you commit resources
OpenQuestions we get asked
Send us the terms of reference.
You will get a method, a named team, a timeline and a fixed price — and an honest opinion on whether this is the right evaluation for your question.
No obligation · Response within one business day · NDA on request