I've come accross the following paragraph in the To Explain or To Predict? paper by Galit Shmueli.
In explanatory modeling, data partitioning is less common [than in predictive modeling] because of the reduction in statistical power. When used, it is usually done for the retrospective purpose of assessing the robustness of ˆf. A rarer yet important use of data partitioning in explanatory modeling is for strengthening model validity, by demonstrating some predictive power. Although one would not expect an explanatory model to be optimal in terms of predictive power, it should show some degree of accuracy.
I understand why data partitioning is useful in the case of a predictive model, which is to assess the generalization capacity of a model. However, in the case of an explanatory model, I don't understand why it should show some degree of accuracy in terms of predictive power since it's not the objective of the model.
Here comes my question: is data partitioning necessary for an explanatory model and why?