Generalized Random Forests Using Fixed-Point Trees
David Fleischer, David A. Stephens, Archer Y. Yang
Abstract
We propose a computationally efficient alternative to generalized random forests (GRFs) for estimating heterogeneous effects in large dimensions. While GRFs rely on a gradient-based splitting criterion, which in large dimensions is computationally expensive and unstable, our method introduces a fixed-point approximation that eliminates the need for Jacobian estimation. This gradient-free approach preserves GRF’s theoretical guarantees of consistency and asymptotic normality while significantly improving computational efficiency. We demonstrate that our method achieves a speedup of multiple times over standard GRFs without compromising statistical accuracy. Experiments on both simulated and real-world data validate our approach. Our findings suggest that the proposed method is a scalable alternative for localized effect estimation in machine learning and causal inference applications.
BibTeX
@inproceedings{
fleischer2025generalized,
title={Generalized Random Forests Using Fixed-Point Trees},
author={David Fleischer and David A. Stephens and Archer Y. Yang},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=1w0Zp99dnX}
}