🔗  AUUC (Area Under the Uplift Curve)

AUUC (Area Under the Uplift Curve)#

A summary of uplift-model quality as the area under its uplift curve.

Important

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What it is#

AUUC (Area Under the Uplift Curve) is the cumulative incremental gain of an uplift model, integrated over the entire population — the total area under the uplift curve (incremental gain against population proportion). It is an overall measure of how well the model ranks individuals by uplift.

How it’s computed#

Sort customers by predicted uplift score (descending) and partition them into bins such as deciles. For each bin, compute the uplift as the treatment response rate minus the control rate,

\[\text{Uplift}_k = \frac{y^{T}_k}{n^{T}_k} - \frac{y^{C}_k}{n^{C}_k},\]

then plot cumulative uplift against the fraction of the population targeted; AUUC is the area under that curve.

The formula#

\[\text{AUUC} = \int_0^1 U(x) \, dx,\]

where \(U(x)\) is the cumulative uplift at population fraction \(x\). A strong model traces a steep curve — targeting the top 20% might capture 80% of all achievable incremental responses — while random targeting hugs the baseline near zero.

AUUC versus the Qini coefficient#

The two are close cousins. AUUC is a raw area, so it depends on dataset size and response rate — much like raw accuracy. The Qini coefficient is a normalised AUUC, scaled between random and perfect targeting, which makes it comparable across datasets — much like AUC. Both are core metrics for evaluating uplift models.


Theme: Causal Inference & Uplift  ·  All terminology



See also

Source article Adapted (context, re-expressed) in our own words from: AUUC (Area Under the Uplift Curve) (insightful-data-lab.com).

Tags: purpose: reference topic: terminology level: advanced