Estimation and inference of heterogeneous treatment effects using random forests S Wager, S Athey Journal of the American Statistical Association 113 (523), 1228-1242, 2018 | 3194 | 2018 |
Generalized random forests S Athey, J Tibshirani, S Wager The Annals of Statistics 47 (2), 1148-1178, 2019 | 2046 | 2019 |
Quasi-oracle estimation of heterogeneous treatment effects X Nie, S Wager Biometrika 108 (2), 299-319, 2021 | 850 | 2021 |
Synthetic difference-in-differences D Arkhangelsky, S Athey, DA Hirshberg, GW Imbens, S Wager American Economic Review 111 (12), 4088-4118, 2021 | 812 | 2021 |
Policy learning with observational data S Athey, S Wager Econometrica 89 (1), 133-161, 2021 | 780* | 2021 |
Dropout training as adaptive regularization S Wager, S Wang, PS Liang Advances in Neural Information Processing Systems, 351-359, 2013 | 739 | 2013 |
Approximate residual balancing: Debiased inference of average treatment effects in high dimensions S Athey, GW Imbens, S Wager Journal of the Royal Statistical Society Series B 80 (4), 597-623, 2018 | 687* | 2018 |
Confidence intervals for random forests: The jackknife and the infinitesimal jackknife S Wager, T Hastie, B Efron The journal of machine learning research 15 (1), 1625-1651, 2014 | 580 | 2014 |
Estimating treatment effects with causal forests: An application S Athey, S Wager Observational Studies 5, 2019 | 456 | 2019 |
High-dimensional asymptotics of prediction: Ridge regression and classification E Dobriban, S Wager The Annals of Statistics 46 (1), 247-279, 2018 | 319 | 2018 |
Local linear forests R Friedberg, J Tibshirani, S Athey, S Wager Journal of Computational and Graphical Statistics 30 (2), 503-517, 2020 | 308 | 2020 |
Adaptive concentration of regression trees, with application to random forests S Wager, G Walther arXiv preprint arXiv:1503.06388, 2015 | 192* | 2015 |
Offline multi-action policy learning: Generalization and optimization Z Zhou, S Athey, S Wager Operations Research 71 (1), 148-183, 2023 | 173 | 2023 |
Sequential selection procedures and false discovery rate control MG G'Sell, S Wager, A Chouldechova, R Tibshirani Journal of the Royal Statistical Society: Series B 78 (2), 423-444, 2016 | 173 | 2016 |
Confidence intervals for policy evaluation in adaptive experiments V Hadad, DA Hirshberg, R Zhan, S Wager, S Athey Proceedings of the national academy of sciences 118 (15), e2014602118, 2021 | 151 | 2021 |
Valuing lead time S De Treville, I Bicer, V Chavez-Demoulin, V Hagspiel, N Schürhoff, ... Journal of Operations Management 32 (6), 337-346, 2014 | 129 | 2014 |
High-dimensional regression adjustments in randomized experiments S Wager, W Du, J Taylor, RJ Tibshirani Proceedings of the National Academy of Sciences 113 (45), 12673-12678, 2016 | 121 | 2016 |
Augmented minimax linear estimation DA Hirshberg, S Wager The Annals of Statistics 49 (6), 3206-3227, 2021 | 118* | 2021 |
Ranger: A fast implementation of random forests MN Wright, S Wager, P Probst R package version 0.12 1, 2020 | 116 | 2020 |
Estimating average treatment effects: Supplementary analyses and remaining challenges S Athey, G Imbens, T Pham, S Wager American Economic Review 107 (5), 278-281, 2017 | 115 | 2017 |