Generalized Random Forests using Fixed-Point Trees
This provides a scalable alternative for localized effect estimation in machine learning and causal inference, though it is incremental as it builds on existing GRF methods.
The paper tackles the computational inefficiency and instability of generalized random forests (GRFs) in high-dimensional settings by introducing a gradient-free fixed-point approximation, achieving a speedup of multiple times without compromising statistical accuracy.
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