ABtree: An Algorithm for Subgroup-Based Treatment Assignment

TL;DR

ABtree is a fast, tree-based algorithm designed to identify subgroups that benefit from specific treatments, enabling personalized treatment decisions to maximize desired outcomes.

Contribution

It introduces a novel, computationally efficient tree method for individualized treatment assignment, focusing on maximizing binary outcomes based on covariates.

Findings

Performs well on simulated data

Effective on real-world datasets

Outperforms existing treatment assignment methods

Abstract

Given two possible treatments, there may exist subgroups who benefit greater from one treatment than the other. This problem is relevant to the field of marketing, where treatments may correspond to different ways of selling a product. It is similarly relevant to the field of public policy, where treatments may correspond to specific government programs. And finally, personalized medicine is a field wholly devoted to understanding which subgroups of individuals will benefit from particular medical treatments. We present a computationally fast tree-based method, ABtree, for treatment effect differentiation. Unlike other methods, ABtree specifically produces decision rules for optimal treatment assignment on a per-individual basis. The treatment choices are selected for maximizing the overall occurrence of a desired binary outcome, conditional on a set of covariates. In this poster, we present the methodology on tree growth and pruning, and show performance results when applied to simulated data as well as real data.