Uplift Modeling uses causal machine learning to estimate how much a campaign truly influences user behavior. Instead of predicting conversions directly, it measures the incremental lift — the difference between users who convert because of an ad and those who would have converted anyway.

In practice, the model analyzes treatment (exposed) and control (unexposed) groups, identifying audience segments most likely to respond positively to advertising. For example, a campaign may reveal that users exposed twice are 40% more likely to purchase than those unexposed.

This method helps advertisers allocate budgets efficiently and focus on impressions that generate real incremental value, not just attributed conversions.