  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).
