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Weather models can't compute some of the important physics that happens at sub-grid scale, and use various tricks to deal with that. How sure are you that these

Weather models can't compute some of the important physics that happens at sub-grid scale, and use various tricks to deal with that. How sure are you that these traditional models aren't also heavily tuned on past data to maximize performance? Perhaps they will also perform badly "out of distribution".

mjburgess 2y ago
Causal physical models have no distributional requirements, so they are not sensitive to distribution shifts. ie., A causal model accounts for all possible distributions. The kind of risk with a causal model is that the model itself, of reality, is incorrect. This is a different kind of risk than there being 'no model at all' as in the case of curve-fitting over historical data, which is radically more fragile to expected shifts. In general, we're quite good at knowing the limitations of causal models, ie., specifying model risk here is much easier. You even, exactly, point out a known problem in the modelling which is impossible to state for an AI model. Since the AI model is just a weak induction, there are no terms/structures/etc. within that model which can be analysed to understand what parts of it are sensitive to what aspects of the system from which the data was taken. All we can say is, we know that in general, train/test distributions have to be "nearly exactly the same" for any of these methods to show anything like cross-val levels of accuracy. So we can very confidently predict when we know train/test wont be the same, that this is a mumbojumbo metric. Indeed, in the vast majority of common ML examples you can, right now, just go and look at real out-sample data collected later than the "common dataset" and you'll find the val accuracy is random or worse-than-random despite arbitarily high cross-val scores. The dataset which drives me most up-the-wall on this is house price datasets, or pricing datasets in general. Prices, generally, follow geometric brownian motion and nearly all ML models are extremelhy bad at modelling prices. So it's basically pseudoscience whenver anyone uses these datasets to demonstrate anything, esp. predictive accuracy.

mjburgess 2y ago

Causal physical models have no distributional requirements, so they are not sensitive to distribution shifts. ie., A causal model accounts for all possible distributions. The kind of risk with a causal model is that the model itself, of reality, is incorrect. This is a different kind of risk than there being 'no model at all' as in the case of curve-fitting over historical data, which is radically more fragile to expected shifts. In general, we're quite good at knowing the limitations of causal models, ie., specifying model risk here is much easier. You even, exactly, point out a known problem in the modelling which is impossible to state for an AI model. Since the AI model is just a weak induction, there are no terms/structures/etc. within that model which can be analysed to understand what parts of it are sensitive to what aspects of the system from which the data was taken. All we can say is, we know that in general, train/test distributions have to be "nearly exactly the same" for any of these methods to show anything like cross-val levels of accuracy. So we can very confidently predict when we know train/test wont be the same, that this is a mumbojumbo metric. Indeed, in the vast majority of common ML examples you can, right now, just go and look at real out-sample data collected later than the "common dataset" and you'll find the val accuracy is random or worse-than-random despite arbitarily high cross-val scores. The dataset which drives me most up-the-wall on this is house price datasets, or pricing datasets in general. Prices, generally, follow geometric brownian motion and nearly all ML models are extremelhy bad at modelling prices. So it's basically pseudoscience whenver anyone uses these datasets to demonstrate anything, esp. predictive accuracy.