Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. 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.

中文翻译：

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局部线性森林

随机森林是一种强大的非参数回归方法，但其拟合平滑信号的能力有限，并且在存在强大平滑效果的情况下可能会显示出较差的预测性能。从随机森林的角度作为自适应核方法，我们将森林核与局部线性回归调整配对，以更好地捕捉平滑度。由此产生的过程，即局部线性森林，使我们能够提高具有平滑信号的随机森林的渐近收敛率，并在真实数据和模拟数据的准确性方面提供显着的提高。我们证明了在森林和平滑约束的规则性条件下有效的中心极限定理，并提出了一个计算有效的置信区间构造。转向因果推理应用程序，我们讨论了异质治疗效果估计的局部回归调整的优点，并给出了一个数据集的例子，探索词选择对社会安全网的态度的影响。最后，我们包括真实数据和生成数据的模拟结果。