 Precisely what the constraints should be on ‘apt’ or ‘appropriate’ models is a matter of ongoing philosophical debate (Blanchard and Schaffer 2017: §1.3). The focus here is on constraints that guarantee that a model does not deliver spurious results (e.g. that Suzy’s throw doesn’t cause the bottle to shatter). However, SEF is also used as a practical tool in scientific inquiry, and this brings additional normative questions into play concerning the choice of variables and their range of permitted values. In the context of assigning blame for the broken bottle, for example, it would not be relevant to include the strength of the bottle’s glass as a variable; by contrast, a local shop owner – tired of Suzy and Billy’s vandalism (they routinely break his shop window as well as any bottles they come across) – might be very interested in the question of what strength of glass might suffice to withstand their rock-throwing. (See e.g. Woodward 2016, Hitchcock 2017, and – for a practical example in the context of causal inference in machine learning – Chalupka, Eberhardt &amp; Perona 2017.) 