"Explain or Predict?" research examines the fundamental and practical differences between using data analytics (statistical models, data mining algorithms, etc.) for prediction compared to causal explanation and to description. Although the discussion of explanation vs. predictions has been actively pursued in the philosophy of science, the statistics literature has not considered it in a holistic way. Yet, statistical modeling can be and is used for each of these goals. The "Explain or Predict?" thesis is that data analytics, from the early stages of study design and data collection to data usage and reporting, takes a different path and leads to different results, depending on whether the goal is predictive or explanatory.
The current conflation of explanation and prediction (in empirical modeling) is pervasive in many fields, and especially in the social sciences. Not only are explanatory power and predictive power confused, but there is a clear lack of adequate predictive modeling in those fields (also due to the under-appreciation of the scientific role of prediction).
This website provides resources on methodology and applications.