grf

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 31 articles , 1 standard article )

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  1. Haupt, Johannes; Lessmann, Stefan: Targeting customers under response-dependent costs (2022)
  2. Huang, Ming-Yueh; Yang, Shu: Robust inference of conditional average treatment effects using dimension reduction (2022)
  3. Tepegjozova, Marija; Zhou, Jing; Claeskens, Gerda; Czado, Claudia: Nonparametric C- and D-vine-based quantile regression (2022)
  4. Wu, Suofei; Hannig, Jan; Lee, Thomas C. M.: Uncertainty quantification for honest regression trees (2022)
  5. Biewen, Martin; Kugler, Philipp: Two-stage least squares random forests with an application to Angrist and Evans (1998) (2021)
  6. Cansu Alakus, Denis Larocque, Aurelie Labbe: RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests (2021) arXiv
  7. Cui, Yifan; Tchetgen Tchetgen, Eric: A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity (2021)
  8. Emmenegger, Corinne; Bühlmann, Peter: Regularizing double machine learning in partially linear endogenous models (2021)
  9. Friedberg, Rina; Tibshirani, Julie; Athey, Susan; Wager, Stefan: Local linear forests (2021)
  10. Hirshberg, David A.; Wager, Stefan: Augmented minimax linear estimation (2021)
  11. Hothorn, Torsten; Zeileis, Achim: Predictive distribution modeling using transformation forests (2021)
  12. Huling, Jared D.; Smith, Maureen A.; Chen, Guanhua: A two-part framework for estimating individualized treatment rules from semicontinuous outcomes (2021)
  13. Li, Sijia; Li, Xiudi; Luedtke, Alex: Discussion of: “More efficient policy learning via optimal retargeting” and “Learning optimal distributionally robust individualized treatment rules”: new objectives for policy learning (2021)
  14. Lu, Benjamin; Hardin, Johanna: A unified framework for random forest prediction error estimation (2021)
  15. Nie, Xinkun; Brunskill, Emma; Wager, Stefan: Learning when-to-treat policies (2021)
  16. Starling, Jennifer E.; Murray, Jared S.; Lohr, Patricia A.; Aiken, Abigail R. A.; Carvalho, Carlos M.; Scott, James G.: Targeted smooth Bayesian causal forests: an analysis of heterogeneous treatment effects for simultaneous vs. interval medical abortion regimens over gestation (2021)
  17. Wang, Tianyu; Morucci, Marco; Awan, M. Usaid; Liu, Yameng; Roy, Sudeepa; Rudin, Cynthia; Volfovsky, Alexander: FLAME: a fast large-scale almost matching exactly approach to causal inference (2021)
  18. Wu, Peng; Xu, Xinyi; Tong, Xingwei; Jiang, Qing; Lu, Bo: Semiparametric estimation for average causal effects using propensity score-based spline (2021)
  19. Yadlowsky, Steve; Pellegrini, Fabio; Lionetto, Federica; Braune, Stefan; Tian, Lu: Estimation and validation of ratio-based conditional average treatment effects using observational data (2021)
  20. Erik Sverdrup; Ayush Kanodia; Zhengyuan Zhou; Susan Athey; Stefan Wager: policytree: Policy learning via doubly robust empirical welfare maximization over trees (2020) not zbMATH

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