Uplift modeling with continuous treatments: A predict-then-optimize approach

TL;DR

This paper introduces a novel predict-then-optimize framework for uplift modeling with continuous treatments, enabling efficient dose allocation by estimating conditional dose responses and solving a dose-allocation problem with integer linear programming.

Contribution

It extends uplift modeling to continuous treatments using causal machine learning and ILP, allowing for resource-aware, fair, and utility-maximizing treatment assignment strategies.

Findings

CADR estimators show trade-offs between policy value and fairness

The ILP-based approach effectively allocates treatment doses

Framework adaptable to healthcare, lending, and HR applications

Abstract

The goal of uplift modeling is to recommend actions that optimize specific outcomes by determining which entities should receive treatment. One common approach involves two steps: first, an inference step that estimates conditional average treatment effects (CATEs), and second, an optimization step that ranks entities based on their CATE values and assigns treatment to the top k within a given budget. While uplift modeling typically focuses on binary treatments, many real-world applications are characterized by continuous-valued treatments, i.e., a treatment dose. This paper presents a predict-then-optimize framework to allow for continuous treatments in uplift modeling. First, in the inference step, conditional average dose responses (CADRs) are estimated from data using causal machine learning techniques. Second, in the optimization step, we frame the assignment task of continuous treatments as a dose-allocation problem and solve it using integer linear programming (ILP). This approach allows decision-makers to efficiently and effectively allocate treatment doses while balancing resource availability, with the possibility of adding extra constraints like fairness considerations or adapting the objective function to take into account instance-dependent costs and benefits to maximize utility. The experiments compare several CADR estimators and illustrate the trade-offs between policy value and fairness, as well as the impact of an adapted objective function. This showcases the framework's advantages and flexibility across diverse applications in healthcare, lending, and human resource management. All code is available on github.com/SimonDeVos/UMCT.