Uplift@k#
The incremental gain captured within the top-k targeted population.
Important
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What it is#
Uplift@k is a performance metric in uplift modelling and causal ML. It measures the incremental effect achieved if you target only the top k% of customers, ranked by the model’s predicted uplift score. In plain terms: if I contact only the top k%, how much extra impact do I get compared to not contacting them?
The formula#
It is the difference in average outcome between the treatment and control groups within the top-k% segment,
where the averages are taken over the top k% by predicted uplift. Random targeting yields a small or zero value (treatment and control behave alike); a good model makes treatment clearly outperform control in that segment.
A worked example#
With 10,000 customers, targeting the top 20% (k = 20%) selects 2,000. If, within that segment, the treatment group’s purchase probability exceeds the control group’s by 5 percentage points, then uplift@k = +5pp — the extra impact the model captures by choosing those 2,000.
Uses, and versus uplift#
In marketing it estimates incremental sales from promoting only the top k%; in healthcare, the incremental recovery from treating the top-k patients; in recommendation, the extra engagement from targeting the top-k users. The distinction from plain uplift is subtle but important: uplift asks how effective is the treatment overall?, while uplift@k asks how good is my model at selecting the best subset to treat?
Theme: Causal Inference & Uplift · All terminology
Hint
Mind map — connected ideas
Uplift Models · Uplift Score · AUUC (Area Under the Uplift Curve) · Qini Curve · Conversion Rate Uplift · Uplift
Hint
More in Causal Inference & Uplift
AUUC (Area Under the Uplift Curve) · Causal Effect · Causal Impact · Causal Inference · Causal ML (Causal Machine Learning) · Causal Trees · Cumulative Incremental Gain (CIG) · Cumulative Uplift · Incremental Conversions · Incremental Gain · Incremental Recovery Rate (IRR) · Incremental Revenue · Incremental Sales · Qini Coefficient
See also
Source article Adapted (context, re-expressed) in our own words from: Uplift@k (insightful-data-lab.com).