Causal approaches to explanation often assume that a model explains by describing features that make a differen ce regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscien ce. She argues that mathematical prin ciples that figure in efficient coding explanations are non-causal differen cemakers. Although these prin ciples cannot be causally altered, efficient coding models can be used to show how would the

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... non change if the prin ciples were modified in counterpossible situations. The problem is that efficient coding models also involve differen ce-makers that, prima facie, cannot be ch aracterized as non-causal in this sense. Mathematical p rin ciples alw ays involve variables which have counterfactual (instead of counterpossible)