In the realm of statistics and machine learning, a lot of discussion has arisen recently around the difference between explaining and predicting: That the two are not the same, and that the difference between the two should be taken into account when choosing what type of model to use, or which model is best amongst a set of competing models, and which metrics should be used to evaluate a model. See this paper by Galit Shmueli for an in-depth discussion. This isn't just a question of transparent statistical models vs. black box machine learning models, the distinction applies even within different types of statistical models. Shmueli for example argues that more complex models are better for prediction, while simpler models are better for explanation.
That in itself surprises me: shouldn't it be the other way around? A simple model is useful for making split decision predictions, while a complex model gives a more nuanced representation of reality, but becomes too unstable and noisy in the process, no?
But that's not what really bugs me. What I can't wrap my head around is how can a theory or model explain the world, without having predictive value? Isn't the fundamental definition of a good explanation, that it is reproducible, i.e. that it predicts future events?
My understanding is that almost all philosophers of science agreed on this: The only way to evaluate a theoretical explanation of empirical facts was by its predictive ability. From the original scientific method of the enlightenment, to the logical positivists, to Popper, all the way to Quine, all seemed to settle on the fact that a scientific explanation of a phenomenon was only as good as the predictions it made. Even Feyerabend, with his epistemological anarchy, argued that science is only as good as it is useful (In some ways Feyerabend was the first black-box machine learning algorithm advocate - any algorithm goes, as long as it give good results on the out of sample test set).
What they disagreed about was mainly the status of science with regards to other intellectual pursuits, and the nature of the epistemological tools that are permissible in science (i.e how to distinguish science from mythology, or from ethics, what is the status of non-observable entities, what constitutes evidence in a scientific context, etc...).
But again they all seemed to agree that a scientific theory must have predictive value. I.e it must conform to the laymen's definition of the experimental method: An explanation of a phenomenon is correct if it successfully predicts the outcome of future experiments. If a theory only explains the past, without (at least partially) predicting the future, then it is either bad science, or unscientific all together.
So with this in mind, here are my questions:
Is my understanding correct that almost all philosophers of science agreed on the idea that a scientific theory (i.e. an explanation of a phenomenon) must have predictive value?
If my above statement is not correct, then what it is the scientific status of a theory that explains but doesn't predict? Wouldn't such a theory fail most demarcation criteria (verification, falsification, Khun's and Lakatos' notions of theories that are progressing, etc....)?
Are Shmueli and other statisticians who argue for the explain vs. predict distinction simply wrong (because they are statisticians, not philosophers or physical scientists)?
P.S: Shmueli mentions in section 1.5 of her paper that this distinction has been discussed in philosophy of science. She cites several authors, but I haven't heard of any of the references besides Hempel and Oppenheim, 1948, and that paper is pretty much in alignment with what I said about the equivalence of explanation and predictive power. I know that ideally I should read the other sources she cited, but I feel that need to wrap my head around the questions I asked here first, especially my second bullet point