Single model approaches
Single model with treatment as feature
The most intuitive and simple uplift modeling technique. A training set consists of two groups: treatment samples and control samples. There is also a binary treatment flag added as a feature to the training set. After the model is trained, at the scoring time it is going to be applied twice: with the treatment flag equals 1 and with the treatment flag equals 0. Subtracting these model’s outcomes for each test sample, we will get an estimate of the uplift.
Hint
In sklift this approach corresponds to the SoloModel class and the dummy method.
Treatment interaction
The single model approach has various modifications. For instance, we can update the number of attributes in the training set by adding the product of each attribute and the treatment flag:
Hint
In sklift this approach corresponds to the SoloModel class and the treatment_interaction method.
References
1️⃣ Lo, Victor. (2002). The True Lift Model - A Novel Data Mining Approach to Response Modeling in Database Marketing. SIGKDD Explorations. 4. 78-86.
Examples using sklift.models.SoloModel
The overview of the basic approaches to solving the Uplift Modeling problem
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