| Use case | Prediction question | Decision question | Main outcome |
|---|---|---|---|
| Offers | Will this customer respond to this offer? | Which eligible action improves margin relative to no offer? | Incremental net contribution per eligible customer |
| Prices | How much demand is expected for this item and context? | Which supported, permitted price improves contribution under demand and inventory constraints? | Net contribution per market/item opportunity over a fixed horizon |
| Churn | Who will leave, and when? | Whose retention improves enough under an action to justify its cost? | Incremental retained contribution after contact and incentive costs |
Prediction estimates outcomes in observed data. Intervention estimates what would change under an action. A model can predict well from patterns that break once you act, as scikit-learn shows in its causal interpretation example. Randomisation is the preferred start for actionable effects. Observational designs need stated identification assumptions and sensitivity analysis, and a more complex learner cannot make up for missing identification.