Generalized Random Forests Using Fixed-Point Trees

David Fleischer, David A. Stephens, Archer Y. Yang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:17262-17305, 2025.

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 this Paper

BibTeX
@InProceedings{pmlr-v267-fleischer25a, title = {Generalized Random Forests Using Fixed-Point Trees}, author = {Fleischer, David and Stephens, David A. and Yang, Archer Y.}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {17262--17305}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/fleischer25a/fleischer25a.pdf}, url = {https://proceedings.mlr.press/v267/fleischer25a.html}, 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.} }
Endnote
%0 Conference Paper %T Generalized Random Forests Using Fixed-Point Trees %A David Fleischer %A David A. Stephens %A Archer Y. Yang %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-fleischer25a %I PMLR %P 17262--17305 %U https://proceedings.mlr.press/v267/fleischer25a.html %V 267 %X 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.
APA
Fleischer, D., Stephens, D.A. & Yang, A.Y.. (2025). Generalized Random Forests Using Fixed-Point Trees. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:17262-17305 Available from https://proceedings.mlr.press/v267/fleischer25a.html.

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