Qini Curve, AUUC & Uplift Evaluation

Uplift Model Offline Evaluation Metrics (Qini Curve, Qini Score, AUUC - Area Under Uplift Curve & Cumulative Gain Charts) solve the fundamental evaluation challenge that individual counterfactual treatment effects are unobservable, rendering classical ROC-AUC/LogLoss metrics inapplicable; Qini construction algorithm: 1) Model Scoring & Sorting: sorting test users in descending order of predicted CATE scores $\hat{\tau}(x)$; 2) Cumulative Segment Tallying: at top $k\%$ cumulative population slice, tallying treatment converters $n_{t, 1}(k)$ and control converters $n_{c, 1}(k)$; 3) Cumulative Qini Metric: $Q(k) = n_{t, 1}(k) - \frac{N_t(k)}{N_c(k)} n_{c, 1}(k)$, quantifying the net incremental conversions directly caused by treatment; 4) AUUC measures the integral area under the Qini curve relative to the diagonal random targeting baseline; higher AUUC indicates superior concentration of persuadable users at the top of the funnel.

Offline Uplift model benchmarking, determining optimal marketing budget cutoff percentiles, and financial incrementality calibration.

Eliminates the fundamental impossibility of computing pointwise MSE against unobserved counterfactuals by measuring group-level incremental lift curves.

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