Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignment

Abstract

Decision making plays a pivotal role in shaping outcomes across various disciplines, such as medicine, economics, and business. This paper provides practitioners with guidance on implementing a decision tree designed to optimise treatment assignment policies through an interpretable and non-parametric algorithm. Building upon the method proposed by Zhou, Athey, and Wager (2023), our policy tree introduces three key innovations: a different approach to policy score calculation, the incorporation of constraints, and enhanced handling of categorical and continuous variables. These innovations enable the evaluation of a broader class of policy rules, all of which can be easily obtained using a single module. We showcase the effectiveness of our policy tree in managing multiple, discrete treatments using datasets from diverse fields. Additionally, the policy tree is implemented in the open-source Python package mcf (modified causal forest), facilitating its application in both randomised and observational research settings.