Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Generalized Random Forests Using Fixed-Point Trees
Authors: David Fleischer, David A. Stephens, Archer Y. Yang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | 1Department of Mathematics and Statistics, Mc Gill University, Montreal, Canada 2Mila Quebec AI Institute, Montreal, Quebec, Canada. Correspondence to: Archer Y. Yang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 The fixed-point tree algorithm... Algorithm 2 Stage I GRF-FPT: Training a generalized random forest using fixed-point trees... Algorithm 3 GRF-FPT: Estimates of ฮธ (x) |
| Open Source Code | Yes | We implement the GRF-FPT algorithm in a fork of grf (Tibshirani et al., 2024) available at https://github.com/dfleis/grf... Code and data for reproducing all experiments and figures are available at https://github.com/dfleis/grf-experiments. |
| Open Datasets | Yes | The data, first appearing in Kelley Pace & Barry (1997), contains 20,640 observations of housing prices taken from the 1990 California census... can be directly obtained from the Carnegie Mellon Stat Lib repository (https://lib.stat.cmu.edu/datasets/houses.zip). |
| Dataset Splits | Yes | Throughout our experiments we use subsampling ratio s/n = 0.5...To assess estimation accuracy, we evaluate the mean squared error (MSE) of ลท(x) across 50 replications of the model and testing on a separate set of 5,000 observations...We varied the subsampling ratio s/n {0.25, 0.50, 0.75} under VCM Setting 3 over a forest of 10 trees carried out using GRF-FPT2 and GRF-grad. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory) are mentioned in the paper, only that the algorithm is implemented in an R package. |
| Software Dependencies | Yes | We implement the GRF-FPT algorithm in a fork of grf (Tibshirani et al., 2024) available at https://github.com/dfleis/grf. R package version 2.4.0. |
| Experiment Setup | Yes | Throughout our experiments we use subsampling ratio s/n = 0.5...All versions fit a forest of 2000 trees, the default settings of the original R implementation (Tibshirani et al., 2024), a subsample ratio of 0.5, and a target minimum node size of 5 observations. |