Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignmen
Type
journal article
Date Issued
2024-07-19
Author(s)
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.
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.
Language
English
Subject(s)