To Explain or to Predict?¶
Shmueli. (2010). To Explain or to Predict?. Statistical Science.
Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Regression
- Fourth, the fitted model supports three structurally distinct uses — prediction (anticipate new outcomes), effect estimation (interpret a coefficient as a causal effect size, which requires identification assumptions external to the fitting procedure), and variance attribution (decompose outcome variance among predictors) — and conflating these three uses is the canonical source of regression misinterpretation.
This sourceAn analysis distinguishing explanatory from predictive modelling and arguing that the two are commonly conflated, with different implications at each step of the modelling process.
Supported in partVerified against the work's full text
“Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description.”
- Fourth, the fitted model supports three structurally distinct uses — prediction (anticipate new outcomes), effect estimation (interpret a coefficient as a causal effect size, which requires identification assumptions external to the fitting procedure), and variance attribution (decompose outcome variance among predictors) — and conflating these three uses is the canonical source of regression misinterpretation.
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