Local Linear Forests

By Rina Friedberg, Julie Tibshirani, Susan Athey, Stefan Wager. 2018

109 citations recorded by the source corpus.

Abstract Random forests are a powerful method for nonparametric regression, but are limited in their ability to fit smooth signals. Taking the perspective of random forests as an adaptive kernel method, we pair the forest kernel with a local linear regression adjustment to better capture smoothness. The resulting procedure, local linear forests, enables us to improve on asymptotic rates of convergence for random forests with smooth signals, and provides substantial gains in accuracy on both real and simulated data. We prove a central limit theorem valid under regularity conditions on the forest and smoothness constraints, and propose a computationally efficient construction for confidence intervals. Moving to a causal inference application, we discuss the merits of local regression adjustments for heterogeneous treatment effect estimation, and give an example on a dataset exploring the effect word choice has on attitudes to the social safety net. Last, we include simulation results on real and generated data. A software implementation is available in the R package grf. Supplementary materials for this article are available online.

Subfields: Statistics and Probability, Model Architectures

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Citations in the corpus

Machine Learning Methods That Economists Should Know About (2019, methodology): “This is also a reason for the superior performance of locally linear random forests (Friedberg et al. [2018]) relative to standard random forests. We discuss four specific sets of methods, although there are many more, including variations on the basic methods. First, we discuss methods where the cl”
Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion) (2020, unknown): “Friedberg et al. (2018) extend this approach to locally linear forests.”