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Enabling Decision Making with the Modified Causal Forest: Policy Trees for Treatment Assignment
- Source :
- Algorithms, Vol 17, Iss 7, p 318 (2024)
- Publication Year :
- 2024
- Publisher :
- MDPI AG, 2024.
-
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.
Details
- Language :
- English
- ISSN :
- 19994893
- Volume :
- 17
- Issue :
- 7
- Database :
- Directory of Open Access Journals
- Journal :
- Algorithms
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.79a4a3aed4c04eebaf4b3a7f50c06b79
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/a17070318