Research Paper#Causal Inference, Policy Learning, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 19:31
Causal-Policy Forest for End-to-End Policy Learning
Published:Dec 28, 2025 09:03
•1 min read
•ArXiv
Analysis
This paper introduces a novel algorithm, the causal-policy forest, for policy learning in causal inference. It leverages the connection between policy value maximization and CATE estimation, offering a practical and efficient end-to-end approach. The algorithm's simplicity, end-to-end training, and computational efficiency are key advantages, potentially bridging the gap between CATE estimation and policy learning.
Key Takeaways
- •Proposes the causal-policy forest, a novel algorithm for policy learning.
- •Connects policy value maximization to CATE estimation.
- •Offers an end-to-end and computationally efficient approach.
- •Aims to bridge the gap between CATE estimation and policy learning.
Reference
“The algorithm trains the policy in a more end-to-end manner.”