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An example I have used a few times to highlight the difference between "understanding how a model gives predictions" and "model uses predictors/features in a way that makes sense" is a linear regression model that Bondarchuk, a famous throwing coach of former USSR.
The linear regression was used to predict the distance expected in one of the throws, given the performances in other athletic tasks, say max squat, max power clean, max long jump and a few others. Some of the regression coefficients were negative, which means that increasing performance in, say, long jump, leads to shorter shot-put (I don't remember which throw the model was for) distances. The model and approach looked understandable and "weird" at the same time.
From a purely statistical perspective it makes sense, since that was the results coming from, I assume, maximum likelihood estimation. From a predictive performance, retrospectively it surely worked because it gave good prediction on past data, assuming there were training and test data (most likely, they were not, but let's assume). But from a future prediction perspective, i.e. the forecasting and thus the manipulation of training to obtain a certain performance, did it make sense? I am very confident it did not, because, among other things, the performances of auxiliary lifts/feats were not independent (you cannot work on a heavier one rep max in the power clean and hope or work toward a shorter long jump performance). The model by itself might have accurate, but considering that interpretable and thus guiding changes in the training program would have been a quite naive mistake. This kinda mistake is quite common among many who think too much about the machinery of the model and way too little about the domain. |
Those are two different questions. For forecasting without manipulation of training it would still make sense. But it wouldn’t make sense for causal analysis.