What are the metrics for evaluating uplift models (Qini, uplift curve)?

Metrics for evaluating uplift models are aimed at measuring the effectiveness of models that predict the difference in behavior between the control and test groups (impact effect). Main metrics:

• Qini curve and Qini coefficient — analogous to the ROC curve for uplift. The Qini curve is constructed based on objects sorted by predicted uplift, showing the cumulative impact effect. The Qini coefficient is the area under the Qini curve, reflecting the model's quality: the higher, the better.

• Uplift curve — a graph showing the accumulated uplift (difference in responses between the test and control groups) as objects with the highest predicted uplift are included. It visually assesses how well the model identifies objects with the maximum effect.

These metrics help understand how effectively the model segments the audience to maximize the impact effect (e.g., a marketing campaign).