Optimal Policy Trees
Abstract
We propose an approach for learning optimal tree-based prescription policies directly from data, combining methods for counterfactual estimation from the causal inference literature with recent advances in training globally-optimal decision trees. The resulting method, Optimal Policy Trees, yields interpretable prescription policies, is highly scalable, and handles both discrete and continuous treatments. We conduct extensive experiments on both synthetic and real-world datasets and demonstrate that these trees offer best-in-class performance across a wide variety of problems.
Cite
Text
Amram et al. "Optimal Policy Trees." Machine Learning, 2022. doi:10.1007/S10994-022-06128-5Markdown
[Amram et al. "Optimal Policy Trees." Machine Learning, 2022.](https://mlanthology.org/mlj/2022/amram2022mlj-optimal/) doi:10.1007/S10994-022-06128-5BibTeX
@article{amram2022mlj-optimal,
title = {{Optimal Policy Trees}},
author = {Amram, Maxime and Dunn, Jack and Zhuo, Ying Daisy},
journal = {Machine Learning},
year = {2022},
pages = {2741-2768},
doi = {10.1007/S10994-022-06128-5},
volume = {111},
url = {https://mlanthology.org/mlj/2022/amram2022mlj-optimal/}
}