For linear models, when the dependent variable y causes the variance of the independent variable x, we know that the reverse causality problem presents, leading to a biased model.
In the case of neural networks, as high-level features/representations are learned from labels (thus the dependent variable), can I say that every NN model suffers the reverse causality problem? I feel subtle about the question and cannot figure it out.
