R package grf: Generalized Random Forests. A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables). This package is currently in beta, and we expect to make continual improvements to its performance and usability.

References in zbMATH (referenced in 15 articles , 1 standard article )

Showing results 1 to 15 of 15.
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  1. Cansu Alakus, Denis Larocque, Aurelie Labbe: RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests (2021) arXiv
  2. Cui, Yifan; Tchetgen Tchetgen, Eric: A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity (2021)
  3. Huling, Jared D.; Smith, Maureen A.; Chen, Guanhua: A two-part framework for estimating individualized treatment rules from semicontinuous outcomes (2021)
  4. Nie, Xinkun; Brunskill, Emma; Wager, Stefan: Learning when-to-treat policies (2021)
  5. Wu, Peng; Xu, Xinyi; Tong, Xingwei; Jiang, Qing; Lu, Bo: Semiparametric estimation for average causal effects using propensity score-based spline (2021)
  6. Yadlowsky, Steve; Pellegrini, Fabio; Lionetto, Federica; Braune, Stefan; Tian, Lu: Estimation and validation of ratio-based conditional average treatment effects using observational data (2021)
  7. Erik Sverdrup; Ayush Kanodia; Zhengyuan Zhou; Susan Athey; Stefan Wager: policytree: Policy learning via doubly robust empirical welfare maximization over trees (2020) not zbMATH
  8. Gubela, Robin M.; Lessmann, Stefan; Jaroszewicz, Szymon: Response transformation and profit decomposition for revenue uplift modeling (2020)
  9. Hothorn, Torsten: Transformation boosting machines (2020)
  10. Mourtada, Jaouad; Gaïffas, Stéphane; Scornet, Erwan: Minimax optimal rates for Mondrian trees and forests (2020)
  11. Wager, Stefan: Comment: Invariance and causal inference (2020)
  12. Wager, Stefan: Cross-validation, risk estimation, and model selection: comment on a paper by Rosset and Tibshirani (2020)
  13. Wu, Peng; Hu, Qi-rui; Tong, Xing-wei; Wu, Min: Learning causal effect using machine learning with application to China’s typhoon (2020)
  14. Athey, Susan; Tibshirani, Julie; Wager, Stefan: Generalized random forests (2019)
  15. Schlosser, Lisa; Hothorn, Torsten; Stauffer, Reto; Zeileis, Achim: Distributional regression forests for probabilistic precipitation forecasting in complex terrain (2019)