Prediction versus Explanation

The distinction between prediction and explanation is one of the foundational problems of Philosophy of Science. A predictive model outputs accurate forecasts about future or unobserved states of a system. An explanatory model says why those states arise — it identifies mechanisms, causes, or structural constraints that make the outcome intelligible rather than merely expected.

The distinction matters because prediction and explanation can come apart. A model that achieves high predictive accuracy on known data distributions — such as AlphaFold predicting protein structures from sequence databases — may do so through statistical correlation with no mechanistic content. Such a model does not explain why the correlation holds, and it will fail precisely where explanations are most needed: on novel inputs, under distributional shift, or where the causal structure changes.

The philosophical framework for this distinction was sharpened by Carl Hempel's Deductive-Nomological model (1948): genuine explanation is a deductive argument from laws plus initial conditions to the explanandum. On this view, prediction and explanation have the same logical structure — they differ only in epistemic context. Critics have challenged this symmetry: explanations require the cited regularities to be genuinely causal, not merely statistical, and they require the regularities to be non-accidentally true. A systems-level view adds a further constraint: explanation must be adequate to the system's level of organization, not merely its micro-level components. See also: Mechanism versus Statistics, Causality, Scientific Realism.