  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.
