I've really been enjoying these two papers since watching a video presentation about the first one:
1. Shmueli, G., "To Explain or To Predict?", Statistical Science, vol. 25, issue 3, pp. 289-310, 2010.
- Video presentation: https://youtu.be/whD2sYFHW8c
2. Shmueli, G., and O. Koppius, "Predictive Analytics in Information Systems Research", MIS Quarterly, vol. 35, issue 3, pp. 553-572, 2011.
PDFs available here: https://www.galitshmueli.com/biblio/term/Explain-Predict
#statistics #theory #modeling #prediction
Assuming that I'm understanding the second paper correctly, it even suggests that we can figure out which areas of theory (its explanatory/causal models) can likely be improved with greater or lesser amounts of additional effort (driving potentially greater research efficiency). This is by comparing the predictive accuracy of explanatory models to the predictive accuracy of predictive models, which are able to pull on potentially more sources of information.
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