I want to describe some of the key challenges to overcome if we are to use causal ML to forecast the weather. These challenges also apply more broadly to the ML forecasting of dynamical systems, but I will focus on the weather as it is an application area which I’m interested in (I work on this) and one where there is a lot of ML progress being made at the moment....
There are a number of reasons we may wish to learn causal mechanisms when modelling a system/forecasting the weather/classifying an image. If our model captures the underlying causal mechanisms, it should be robust to new scenarios (e.g. the future), and it should still produce sensible results if we alter the input (“make an intervention”). Intervening on a system and seeing how things end up helps us make decisions. The issue is that the majority of existing ML tools simply learn correlations....