The Statistical Modeling, Causal Inference, and Social Science Blog has a post on an interesting paper by J.M. Epstein concerning modeling. Here’s an excerpt:
In summary, while most mathematical treatment of statistical modeling tends to be focused purely on prediction, there is a good reason why the cost of interpretation should be considered. Epstein’s list of why interpretability matters should motivate us to care:
1. Explain (very distinct from predict)
2. Guide data collection
3. Illuminate core dynamics
4. Suggest dynamical analogies
5. Discover new questions
6. Promote a scientific habit of mind
7. Bound (bracket) outcomes to plausible ranges
8. Illuminate core uncertainties.
9. Offer crisis options in near-real time
10. Demonstrate tradeoffs / suggest efficiencies
11. Challenge the robustness of prevailing theory through perturbations
12. Expose prevailing wisdom as incompatible with available data
13. Train practitioners
14. Discipline the policy dialogue
15. Educate the general public
16. Reveal the apparently simple (complex) to be complex (simple)
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