Uplift Modeling with Multiple Treatments and General Response Types
This work addresses the challenge of personalizing treatment assignments in randomized experiments for decision-making, offering a solution that applies to various treatments and response types, but it is incremental as it builds on existing uplift modeling techniques.
The paper tackles the problem of uplift modeling with multiple treatments and general response types by proposing a new algorithm that creates a forest of randomized trees with a splitting criterion optimized for uplift performance, and it introduces an unbiased evaluation method; experimental results show significant performance improvement over other methods.
Randomized experiments have been used to assist decision-making in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods.