“Realist” evaluation and the more general “theory‑based” evaluation umbrella often present themselves as distinct forms of evaluation; however, their core assumptions that programmes work (or not) through underlying causal mechanisms that cannot be directly observed are restatements of what sciences already presuppose. It’s rare indeed for an evaluation not to begin with a theory of change; the issue is not whether that’s necessary but how good theories are. We’ve all seen boxology lists in the logic model style.
The full range of evaluation methods can test mechanisms rather than only whether an outcome has been shifted by a programme and current practice involves qual and quant evidence, e.g., the latter using mediation tests. The logic of “what works best for whom in what context” predates “realist” approaches by a few decades and is used across all methods. Similarly, there’s nothing uniquely “realist” about context-mechanism-outcome triads: it’s a useful way to move from boxology to theory prose.
Advocating “realist” or “theory‑based” evaluation is essentially advocating “scientific evaluation” – a kind of tautology. It may be helpful for some readers to learn about separating ontology from epistemology (the map is not the territory, to paraphrase Korzybski), but I’d suggest that’s best left to the first chapter of an introduction to evaluation rather than something that needs to be restated in every report. There are examples of fields that stick on the “science” term: neuroscience and cognitive science are two examples. But what would unscientific policy evaluation look like? What does it get called? (That’s a genuine question – I have a blog post brewing.)