Causal-Policy Forest for End-to-End Policy Learning

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.