GENERALIZED RANDOM FORESTS
GENERALIZED RANDOM FORESTS
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DOI：
10.1214/18-aos1709
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发表时间：
2019-04-01
影响因子：
4.5
通讯作者：
Wager, Stefan
中科院分区：
文献类型：
--
作者：
Athey, Susan;Tibshirani, Julie;Wager, Stefan
我们提出了广义随机森林，一种基于随机森林（Breiman [Mach.学习. 45（2001）5-32]），其可用于拟合被识别为局部矩方程组的解的任何感兴趣的量。在局部最大似然估计的文献之后，我们的方法考虑了附近训练示例的加权集;然而，我们使用的不是容易产生强烈维数灾难的经典核加权函数，而是来自森林的自适应加权函数，该森林旨在表达指定数量的异质性。我们提出了一个灵活的，计算效率高的算法增长的广义随机森林，开发一个大样本理论，我们的方法表明，我们的估计是一致的，渐近高斯，并提供了一个估计其渐近方差，使有效的置信区间。我们使用我们的方法来开发三个统计任务的新方法：非参数分位数回归，条件平均部分效应估计和异质性治疗效果估计通过工具变量。一个软件实现，R和C++的grf，可从CRAN获得。
We propose generalized random forests, a method for nonparametric statistical estimation based on random forests (Breiman [Mach. Learn. 45 (2001) 5-32]) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method considers a weighted set of nearby training examples; however, instead of using classical kernel weighting functions that are prone to a strong curse of dimensionality, we use an adaptive weighting function derived from a forest designed to express heterogeneity in the specified quantity of interest. We propose a flexible, computationally efficient algorithm for growing generalized random forests, develop a large sample theory for our method showing that our estimates are consistent and asymptotically Gaussian and provide an estimator for their asymptotic variance that enables valid confidence intervals. We use our approach to develop new methods for three statistical tasks: nonparametric quantile regression, conditional average partial effect estimation and heterogeneous treatment effect estimation via instrumental variables. A software implementation, grf for R and C++, is available from CRAN.