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

TL;DR

This paper introduces an interpretable policy tree algorithm for treatment assignment, extending the Modified Causal Forest method to handle multiple treatments and variables, with practical implementation in Python.

Contribution

It presents a novel policy tree approach based on the Modified Causal Forest, incorporating constraints and supporting categorical and continuous variables for treatment decision-making.

Findings

Effective in multiple treatment scenarios

Handles categorical and continuous variables

Available as open-source in Python

Abstract

Decision-making plays a pivotal role in shaping outcomes in various disciplines, such as medicine, economics, and business. This paper provides guidance to practitioners on how to implement a decision tree designed to address treatment assignment policies using an interpretable and non-parametric algorithm. Our Policy Tree is motivated on the method proposed by Zhou, Athey, and Wager (2023), distinguishing itself for the policy score calculation, incorporating constraints, and handling categorical and continuous variables. We demonstrate the usage of the Policy Tree for multiple, discrete treatments on data sets from different fields. The Policy Tree is available in Python's open-source package mcf (Modified Causal Forest).