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.

翻译：提升建模在市场营销、政策制定及个性化推荐等多种应用中至关重要，其主要目标是学习针对异质性人群的最优处理分配方案。现有工作的一支主要方向通过修改决策树算法的损失函数，来识别具有异质处理效应的群组；另一支方向则利用现成监督学习算法，分别对处理组和对照组估计个体处理效应。实践中，直接建模异质处理效应的前一种方法通常优于后者。然而，现有基于树的方法大多局限于单处理与单对照用例，仅有少数扩展至多离散处理场景。本文提出一种树方法的泛化方案，以应对多重离散和连续值处理场景。我们聚焦于对经典因果树算法的泛化——因其具有良好的统计性质，但我们的泛化技术也可应用于其他基于树的方法。通过实验和真实数据实例，验证了所提方法的有效性。