Did the grant cause the result? Attribution methods from before-and-after to contribution analysis

Attribution asks whether the funded project caused an observed outcome. No method is best in every case. Choose according to the decision, risk, available comparison, data quality, ethics, cost and project scale.

This comparison adapts public-sector evaluation concepts for business use. It does not claim that a simple internal review meets formal government evaluation standards.

What to take away

• No single attribution method is best; choose based on decision, risk, comparison, data, ethics, cost and scale.
• Before-and-after monitoring shows change but cannot prove the grant caused the outcome.
• Target-versus-actual comparison shows performance against plan, not attribution to the grant.
• Contribution analysis supports a reasoned claim about the project's role, not a precise causal percentage.
• Experimental designs require specialist design, adequate sample size and ethical consideration.

Before-and-after monitoring

Measure the same outcome before and after delivery. This is inexpensive and easy to explain. It can show change, but demand, seasonality, prices, staffing and other investments may also explain the difference.

Use several pre-project periods where possible and document concurrent changes. Describe the result as observed change rather than caused impact.

Target-versus-actual comparison

Compare the measured result with the application target. This shows performance against plan, not attribution. A target may be based on weak assumptions, and exceeding it does not prove that the grant made the difference.

Preserve the original target and method. Do not revise it after seeing actual performance.

Matched operational comparison

Compare a funded site, product or group with a sufficiently similar unfunded one. Matching can improve the estimate, but hidden differences may remain. Define selection criteria before analysis and test whether baseline trends are comparable.

Avoid choosing the comparison after results appear. That invites a favourable but misleading match.

Experimental or quasi-experimental evaluation

Experimental designs allocate exposure, often randomly. Quasi-experimental designs construct a comparison from non-random data using stated assumptions. Both require specialist design, adequate sample size and ethical consideration.

The 2026 Magenta Book explains that experimental and quasi-experimental approaches use an unaffected group or period as a counterfactual. It also stresses suitable data quality and comparability.

These designs may be disproportionate or infeasible for one small grant project. Seek an evaluation specialist before promising causal estimates.

Contribution analysis

Build a theory of how activities should lead to outcomes, collect evidence for each link and test alternative explanations. This can suit complex projects where one intervention operates with several influences.

Contribution analysis supports a reasoned claim about the project's role, not a precise causal percentage by default. Include contradictory evidence and conditions under which the result occurred.

Qualitative comparative evidence

Interviews, observation and case records can explain how delivery worked, who benefited and why. They add context but do not turn a selected success story into a representative impact estimate.

Use a documented sampling and analysis method. Protect participants and report negative or mixed accounts.

Choosing proportionately

The government's evaluation registry guidance says government evaluation should be proportionate, with lighter monitoring for low-risk, well-evidenced activity and greater scrutiny for high-risk or novel interventions. Its registry duties apply to specified government evaluations, not automatically to small grant recipients.

Compare methods before committing

Score each feasible method against the same criteria: question answered, comparison strength, data already available, new collection, sample, delivery disruption, privacy, specialist skill, cost and timing. Reject a sophisticated design if the project cannot implement it faithfully.

Run a feasibility check before promising an impact result to a funder. Confirm that enough units exist, the proposed comparison will remain uncontaminated and the outcome can be measured at the right time. Where these conditions fail, redesign the question or state that the project will monitor outcomes and investigate contribution.

Preserve the agreed protocol before analysing data. Record departures and their effect instead of quietly choosing the method that produces the most favourable result.

Write the evaluation question first. Then record the selected method, counterfactual or comparison, assumptions, limitations, analyst and review plan. Do not use the word "caused" when the method establishes only timing or contribution.

This draft contains no live internal links and needs specialist methodological review before publication or use in a formal evaluation.

Before you act

• Write the evaluation question first.
• Record the selected method and its assumptions.
• Preserve the agreed protocol before analysing data.
• Run a feasibility check before promising an impact result.
• Avoid choosing the comparison after results appear.
• Do not use the word caused when the method establishes only timing or contribution.

Common questions

What does before-and-after monitoring show and not show?

It measures the same outcome before and after delivery and can show change. However, demand, seasonality, prices, staffing and other investments may also explain the difference. The text advises describing the result as observed change rather than caused impact.

When is contribution analysis suitable?

It can suit complex projects where one intervention operates with several influences. It involves building a theory of how activities lead to outcomes, collecting evidence for each link and testing alternative explanations. It supports a reasoned claim about the project's role, not a precise causal percentage by default.

What should be done before committing to an attribution method?

Score each feasible method against criteria such as question answered, comparison strength, data already available, new collection, sample, delivery disruption, privacy, specialist skill, cost and timing. Run a feasibility check before promising an impact result to a funder. Preserve the agreed protocol before analysing data.