Distinguishing between statistical models used for prediction and those used for explanation
In short: A theoretical paper in Statistical Science clarifies the vital distinction between using models to predict future outcomes and using them to find causal explanations. The author argues that many researchers mistakenly assume that a model with high explanatory power will naturally possess high predictive power.
The article explores how conflating these two goals can hinder scientific progress. By examining the different requirements for each modeling approach, the author highlights the practical implications for every stage of the modeling process, though the paper focuses on conceptual clarification rather than presenting new empirical data.
Why it matters: Understanding the difference between explaining why something happens and predicting what will happen is essential for scientific progress.
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Sources
- Summary by Intellect · source: Research: Shmueli (2010). To Explain or to Predict?. Statistical Science.
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