Adapting Neural Networks for Uplift Models
This work addresses overfitting issues in uplift modeling for marketing applications, representing an incremental improvement over existing tree-based methods.
The paper tackles the problem of overfitting in uplift models, which estimate individual treatment effects for marketing interventions, by proposing a neural network method that jointly optimizes conditional mean and transformed outcome losses. The method improves state-of-the-art performance on synthetic and real data.
Uplift is a particular case of individual treatment effect modeling. Such models deal with cause-and-effect inference for a specific factor, such as a marketing intervention. In practice, these models are built on customer data who purchased products or services to improve product marketing. Uplift is estimated using either i) conditional mean regression or ii) transformed outcome regression. Most existing approaches are adaptations of classification and regression trees for the uplift case. However, in practice, these conventional approaches are prone to overfitting. Here we propose a new method using neural networks. This representation allows to jointly optimize the difference in conditional means and the transformed outcome losses. As a consequence, the model not only estimates the uplift, but also ensures consistency in predicting the outcome. We focus on fully randomized experiments, which is the case of our data. We show our proposed method improves the state-of-the-art on synthetic and real data.