Abstract
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.
Highlights
Experiments and models are routinely used as surrogate systems in the social sciences (Mäki 2009): they are studied independently of target systems of interest, allow us to isolate phenomena and exercise discriminate control overReceived 1 March 2021 1Leibniz University Hannover, GermanyThe existing literature has detailed how experiments and models, respectively, relate to targets, permit learning about them, and how their epistemic capabilities can differ
I have argued that an important class of experiment-target inference is not well understood as proceeding from an experimental system to a target directly, but rather as a hybrid inference, Mediated Experiment-Target Inference (METI), which involves experiments and models
Argued that extrapolation requires models for two reasons: first, because we need to make at least some implicit causal assumptions, and second, because good, explicit models play essential roles in facilitating successful extrapolation: they guide the search for relevant similarities and differences, encode such information at an epistemic level, and help with deriving predictions about the effects of ultimate interest
Summary
Experiments and models are routinely used as surrogate systems in the social sciences (Mäki 2009): they are studied independently of target systems of interest, allow us to isolate phenomena and exercise discriminate control over. To extrapolate a causal effect from a study population to a novel target, one needs to support that populations are sufficiently similar, or account for how they differ. This is where models play important but underappreciated roles (see Cartwright and Stegenga 2011). Good causal models of both populations are needed for (1) guiding the search for similarities and differences, (2) making these accessible to investigators, and (3) deriving predictions about the effects of interest These crucial roles for models suggest that it is unhelpful to understand extrapolation as an inference proceeding directly from an experiment to a target.
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