2016/05/13 by Derek Feng, Xiaofei Wang, Feng, Derek +1
Business, Management and Accounting · Economics, Econometrics and Finance · Mathematics · #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Game Theory and Voting Systems #Machine Learning (stat.ML) #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.1605.04262
openalex publication_date 2016/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.