> For the complete documentation index, see [llms.txt](https://eval.playbook.org.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://eval.playbook.org.ai/social-impact/overview/why-is-this-level-of-evaluation-important.md).

# Who is involved in this evaluation?

<table><thead><tr><th valign="top">Execute</th><th valign="top">Support</th></tr></thead><tbody><tr><td valign="top"><strong>Policy Researchers, some Data Scientists, and/or Economists</strong></td><td valign="top"><strong>AI Engineers</strong></td></tr><tr><td valign="top">Apply evaluation methods with the proper measurement tools</td><td valign="top">Ensure that the product functions as expected throughout the evaluation phase</td></tr></tbody></table>

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#### Key Motivation

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

<table data-view="cards"><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td><h4><i class="fa-arrow-up-right-dots">:arrow-up-right-dots:</i></h4></td><td><strong>Causal Attribution</strong></td><td>It proves that improvements were caused by your product, not by coincidence or external trends.</td></tr><tr><td><h4><i class="fa-arrows-maximize">:arrows-maximize:</i></h4></td><td><strong>Informing Scale</strong></td><td>It provides the cost-effectiveness data needed to justify large-scale budget allocations.</td></tr><tr><td><h4><i class="fa-rotate">:rotate:</i></h4></td><td><strong>Identifying Unintended Effects</strong></td><td>Rigorous trials can surface hidden negative consequences or surprising positive spillovers that simpler metrics miss.</td></tr></tbody></table>

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### 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[^1].

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](https://www.nature.com/articles/d41586-025-02266-7.epdf?sharing_token=jCKO3Tx8dFeQfucqP5VCcNRgN0jAjWel9jnR3ZoTv0PS1htX8Sko7IudKf1MVjrKQ-g3NeuYAsnuJ-Io9wHN3uMBrjSLLnu_wjpJLF2G-unWgOw27UqLqC_yalnt2AFTYmMZAO31agMcWvNwKRpfYsfrMt3fmIKm0iVbftxqAsY%3D); [UK GOV, 2025](https://www.gov.uk/government/publications/the-magenta-book/guidance-on-the-impact-evaluation-of-ai-interventions-html)).

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.<br>

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[^1]: Selecting unintended consequences to measure can be informed by process evaluations or level 3 data to minimize cost of data collection.