Generalized Causal Tree for Uplift Modeling
This work addresses a problem in marketing, policy-making, and personalized recommendations by extending uplift modeling to more complex treatment scenarios, though it is incremental as it builds on existing causal tree methods.
The paper tackles the limitation of existing tree-based uplift modeling methods, which are mostly restricted to single treatments, by proposing a generalization that handles multiple discrete and continuous-valued treatments, demonstrating efficacy through experiments and real data examples.
Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment allocations for a heterogeneous population. A primary line of existing work modifies the loss function of the decision tree algorithm to identify cohorts with heterogeneous treatment effects. Another line of work estimates the individual treatment effects separately for the treatment group and the control group using off-the-shelf supervised learning algorithms. The former approach that directly models the heterogeneous treatment effect is known to outperform the latter in practice. However, the existing tree-based methods are mostly limited to a single treatment and a single control use case, except for a handful of extensions to multiple discrete treatments. In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments. We focus on a generalization of the well-known causal tree algorithm due to its desirable statistical properties, but our generalization technique can be applied to other tree-based approaches as well. The efficacy of our proposed method is demonstrated using experiments and real data examples.