Practical thoughts on explanatory vs. predictive modeling
This question has been bugging me for some time, and I was going to write a blog post about it. However, I think it is better left for discussion in this forum.
Back in April, I attended a talk at the UMD Math Department Statistics group seminar series called "To Explain or To Predict?". The talk was given by Prof. Galit Shmueli who teaches at UMD's Smith Business School. Her talk was based on research she did for a paper titled "Predictive vs. Explanatory Modeling in IS Research", and a follow up working paper titled "To Explain or To Predict?".
Dr. Shmueli's argument is that the terms "predictive" and "explanatory" in a statistical modeling context have become conflated and that statistical literature lacks a thorough discussion of the differences. In the paper, she contrasts both and talks about their practical implications. I encourage you to read the papers.
The question I'd like to pose to the practitioner community is: have you ever fallen into the trap of using one when meaning to use the other? I certainly have. How do you know which one to use? How do you define a predictive exercise versus an explanatory/descriptive one? It would be useful if you could talk about the specific application.