Generalized Random Forests using Fixed-Point Trees
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
Cite
@article{arxiv.2306.11908,
title = {Generalized Random Forests using Fixed-Point Trees},
author = {David Fleischer and David A. Stephens and Archer Y. Yang},
journal= {arXiv preprint arXiv:2306.11908},
year = {2025}
}
Comments
44 pages, 17 figures