For the complete documentation index, see llms.txt. This page is also available as Markdown.

Who is involved in this evaluation?

Execute
Support

Policy Researchers, some Data Scientists, and/or Economists

AI Engineers

Apply evaluation methods with the proper measurement tools

Ensure that the product functions as expected throughout the evaluation phase


Key Motivation

Policy makers, donors, and governments require credible evidence before they invest in scaling a solution. Level 4 evaluation is critical because:

Causal Attribution

It proves that improvements were caused by your product, not by coincidence or external trends.

Informing Scale

It provides the cost-effectiveness data needed to justify large-scale budget allocations.

Identifying Unintended Effects

Rigorous trials can surface hidden negative consequences or surprising positive spillovers that simpler metrics miss.


Why is this level of evaluation important?

The central reason to do an IE is to inform policymakers, donors and implementers on whether and how to incorporate an intervention in their plans. By isolating the intervention from other influences, impact evaluation enables causal attribution of outcome changes. Once effectiveness is established for a specific setting and population, additional evaluations can test whether it works elsewhere or for other groups. Moreover, since impact evaluations isolate causal effects, they are ideal for measuring unintended (as well as intended) impacts of an intervention.

For many funders and public sector partners, IEs are central to decision-making. They seek credible evidence that a product improves lives—beyond engagement metrics or self-reports—before scaling. A well-designed IE signals real-world effectiveness and the likelihood of meaningful social returns (see e.g. Hauser et al., 2025; UK GOV, 2025).

IEs also help funders compare options. Combined with cost data, they enable cost-effectiveness and cost-benefit analysis—critical when governments, donors, and multilaterals allocate scarce resources. In many cases, IE results directly inform decisions to scale, replicate, or exit.


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