This question references Galit Shmueli's paper "To Explain or to Predict".
Specifically, in section 1.5, "Explaining and Prediction are Different", Professor Shmueli writes:
In explanatory modeling the focus is on minimizing bias to obtain the most accurate representation of the underlying theory.
This has puzzled me each time I've read the paper. In what sense does minimizing the bias in estimates give the most accurate representation of the underlying theory?
I also watched professor Shmueli's talk here, delivered at JMP Discovery Summit 2017, and she states:
...things that are like shrinkage models, ensembles, you will never see those. Because those models, by design, introduce bias in order to reduce the overall bias/variance. That's why they won't be there, it doesn't make any theoretical sense to do that. Why would you make your model biased on purpose?
This doesn't really shed light on my question, simply restating the claim that I don't understand.
If the theory has many parameters, and we have scant data to estimate them, the estimation error will be dominated by variance. Why would it be inappropriate to use a biased estimation procedure like ridge regression (resulting in biased estimates of lower variance) in this situation?