would be to ensure respective extrapolation properties by selecting the hypothesis class F accordingly. Indeed, in our experiments in Sections 3.2 and 3.3, we chose tree-based models; had we chosen linear models, extrapolation would have been less critical, and the agreement between the expectation and the result from meteorological normalization would have been better. (Situations such as the one illustrated in Section 3.1 would still appear, however.) Choosing an appropriate inductive bias requires a deep understanding of the relationship one intends to learn, which is often not possible in a purely data-driven 250 setting (but which may be achievable in theory-inspired settings when prior knowledge is available, cf. ).