Uplift Modeling with Multiple Treatments and General Response Types

TL;DR

This paper introduces a new uplift modeling algorithm using randomized trees that optimizes treatment effects for heterogeneous subjects, applicable to multiple treatments and response types, with an unbiased evaluation method.

Contribution

It proposes a novel uplift algorithm with a splitting criterion tailored for multiple treatments and general responses, along with an unbiased performance evaluation method.

Findings

Significant performance improvements over existing methods.

Effective handling of multiple treatments and response types.

Validated on synthetic and real industry data.

Abstract

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.