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grf: Generalized Random Forests
Julie Tibshirani, Susan Athey, Erik Sverdrup, Stefan Wager
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摘要与影响
摘要 · 完整
Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.
逐年被引趋势
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关键指标
87
被引次数 · OpenAlex
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领域内被引倍数
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同类平均 = 1
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学术脉络
学科主题
计算机 / AIMachine Learning and Data Classification
Neural Networks and Applications · Data Mining Algorithms and Applications
参考文献 21
Cross-Validated Targeted Minimum-Loss-Based Estimation
被引 180Wenjing Zheng, Mark J. van der Laan · Springer series in statistics · 2011
Estimation of Regression Coefficients When Some Regressors are not Always Observed
被引 2,785James M. Robins, Andrea Rotnitzky, Lue Ping Zhao · Journal of the American Statistical Association · 1994
Model-Based Recursive Partitioning
被引 789Achim Zeileis, Torsten Hothorn, Kurt Hornik · Journal of Computational and Graphical Statistics · 2008
此处列出前 3 条
引用本文 87
Machine Learning Methods That Economists Should Know About
被引 1,097Susan Athey, Guido W. Imbens · Annual Review of Economics · 2019
Recent cover crop adoption is associated with small maize and soybean yield losses in the United States
被引 170Jillian M. Deines, Kaiyu Guan, Bruno Lopez · Global Change Biology · 2022
Performance of NO, NO 2 low cost sensors and three calibration approaches within a real world application
被引 148Alessandro Bigi, Michael Mueller, Stuart K. Grange · Atmospheric measurement techniques · 2018
按被引量排序，此处列出前 3 条