Uplift modeling techniques can be grouped into data preprocessing and data processing approaches.
In the preprocessing approaches, existing out-of-the-box learning methods are used, after pre- or post-processing of the data and outcomes.
A popular and generic data preprocessing approach is the flipped label approach, also called class transformation approach.
Other data preprocessing approaches extend the set of predictor variables to allow for the estimation of uplift. An example is the single model with treatment as feature.
In the data processing approaches, new learning methods and methodologies are developed that aim to optimize expected uplift more directly.
Data processing techniques include two categories: indirect and direct estimation approaches.
Indirect estimation approaches include the two-model model approach.
Direct estimation approaches are typically adaptations from decision tree algorithms. The adoptions include modified the splitting criteria and dedicated pruning techniques.
1️⃣ Devriendt, Floris, Tias Guns and Wouter Verbeke. “Learning to rank for uplift modeling.” ArXiv abs/2002.05897 (2020): n. pag.