# 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 [PERSON], [PERSON], and [PERSON] (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.  
causal machine learning; conditional average treatment effects; multiple treatments; offline policy learning; decision trees  
[PERSON] and [PERSON], F., [PERSON], [PERSON].  
(CAIPWL) policy learner, which achieves optimal convergence rates in multi-action settings with unknown propensity scores.  
This work presents the _mcf_ policy tree as implemented in the _mcf_ package. The policy learner is based on the algorithm proposed by [9] but introduces three key modifications: policy scores calculation, implementation of constraints, and different handling of categorical and continuous variables.  
The first modification is in policy score calculation. The _mcf_ computes consistent and asymptotically normally distributed policy scores to estimate individualised average treatment effects (IATEs) [10]. These scores differ from the doubly robust scores used in the original algorithm, as the _mcf_ algorithm minimises estimation errors of the conditional mean responses and their covariance while also considering propensity score heterogeneity to reduce selection bias. Unlike doubly robust scores, the IATEs depend only on features, not outcomes or treatments. The second modification concerns the implementation of constraints. Constraints are incorporated as treatment-specific cost values, subtracted from the policy scores. This also applies to constraints indirectly related to costs, such as treatment share limitations, ensuring comprehensive decision-making considerations. The third modification is the handling of categorical and continuous variables. Traditional methods often use one-hot encoding for categorical features, leading to extreme leaves with many values in shallow trees. The _mcf_ policy tree, however, allows several categories on both sides of a split, thus avoiding such issues (see Chapter 9.2.4 in [11] for a detailed explanation of this approach). For continuous and unordered features with many values, users can specify an approximation parameter to determine potential splitting points, providing finer granularity compared to the single global approximation parameter A in [9]. For instance, considering a four-level tree, the number of splitting points is A/8 at the 4 th level (the top of the tree), A/4 at the 3 rd level, A/2 at the 2 nd level, and A at the 1 st level (the bottom of the tree).  
To the best of our knowledge, the _mcf_ policy tree algorithm is the first method to integrate all these features into a single optimal policy module. Consequently, our work introduces a more holistic and modern policy learner, demonstrating its versatility across various fields. By doing so, we aim to facilitate its application for practitioners, providing them with a comprehensive and advanced tool for policy analysis.  
In the remainder of this paper, Section 2 introduces the framework for identification, estimation, and package functionalities. Section 3 shows the setups and findings of the empirical studies. Finally, Section 4 discusses computational facets and concludes.