This paper argues that an important type of experiment-target inference, extrapolating causal effects, requires models to be successful. Focusing on extrapolation in Evidence-Based Policy, it is argued that extrapolation should be understood not as an inference from an experiment to a target directly, but as a hybrid inference that involves experiments and models. A general framework, METI, is proposed to capture this role of models, and several benefits are outlined: (1) METI highlights epistemically significant interactions between experiments and models, (2) reconciles some differences among existing accounts of experiment-target relationships, and (3) facilitates critical appraisal of inferential practices from experiments.

本文认为，推断目标因果关系是一种重要的实验目标推论，需要模型成功。围绕基于证据的政策中的外推法，有人认为外推法不应被理解为直接从实验到目标的推论，而应被理解为涉及实验和模型的混合推论。提出了一个通用框架METI来捕捉模型的这种作用，并概述了几个好处：（1）METI强调了实验与模型之间在认识论上的重要交互作用；（2）调和了现有的实验-目标关系之间的某些差异，以及（3）促进对实验推论实践的批判性评估。