Thomas Lumley • 5/2/2021

Generalisability, prediction, and causation

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This article discusses a key statistical concept: the difference between using regression models for causal inference versus predictive inference. It argues that causality is not essential for prediction when data is a simple random sample, but generalizability becomes crucial when predicting for new data or populations. The piece uses examples like credit scores and insurance risk to illustrate stable associations versus causal relationships.

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#Machine Learning #statistics #Data Science
Generalisability, prediction, and causation

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