Generalized Causal Tree for Uplift Modeling

TL;DR

This paper introduces a generalized causal tree method capable of handling multiple discrete and continuous treatments, improving uplift modeling across diverse applications with demonstrated effectiveness through experiments and real data.

Contribution

It extends existing causal tree algorithms to support multiple and continuous treatments, broadening their applicability in uplift modeling.

Findings

Effective in handling multiple treatments

Outperforms existing methods in experiments

Validated on real-world data

Abstract

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.