 So what are the constraints on causal models, such that they accurately represent the causal facts that we’re interested in (Halpern and Hitchcock 2010: §§4–5)? Various authors have proposed constraints that tell us what count as (to use Hitchcock’s term) ‘apt’ models, many of which are analogues of Lewis’s constraints and for the same reasons, namely to ensure that there is no spurious counterfactual dependence. Thus Hitchcock (2001: 287) proposes that the values of variables should not represent events that bear logical or metaphysical relations to one another, and Blanchard and Schaffer (2017: 182) propose that the values allotted should represent intrinsic characterisations. Hitchcock (2001: 287) also proposes that the variables should not be allotted values ‘that one is not willing to take seriously’ (about which more below). Halpern and Hitchcock (2010) add a ‘stability’ constraint: adding additional variables should not overturn the causal verdicts. (This constraint addresses the problem of the ‘model’ described above that just includes ST and BS; that model delivers a verdict, namely that Suzy’s throw doesn’t cause the bottle to shatter, which is overturned by adding additional variables.) And Hitchcock (2007: 503) proposes the constraint that the model “should include enough variables to capture the essential structure of the situation being modeled”. (Though if one had reductionist aspirations, this constraint would appear to render one’s analysis of causation viciously circular, since the ‘essential structure’ of the situation is presumably its essential causal structure – just what a causal model is supposed to deliver.)