| A robust method for estimating optimal treatment regimes B Zhang, AA Tsiatis, EB Laber, M Davidian Biometrics 68 (4), 1010-1018, 2012 | 598 | 2012 |
| Precision medicine MR Kosorok, EB Laber Annual review of statistics and its application 6 (1), 263-286, 2019 | 456 | 2019 |
| Estimating optimal treatment regimes from a classification perspective B Zhang, AA Tsiatis, M Davidian, M Zhang, E Laber Stat 1 (1), 103-114, 2012 | 371 | 2012 |
| Q-learning: Theory and applications J Clifton, E Laber Annual Review of Statistics and Its Application 7 (1), 279-301, 2020 | 357 | 2020 |
| New statistical learning methods for estimating optimal dynamic treatment regimes YQ Zhao, D Zeng, EB Laber, MR Kosorok Journal of the American Statistical Association 110 (510), 583-598, 2015 | 342 | 2015 |
| Q-and A-learning methods for estimating optimal dynamic treatment regimes PJ Schulte, AA Tsiatis, EB Laber, M Davidian Statistical science: a review journal of the Institute of Mathematical …, 2015 | 328 | 2015 |
| The ecology of microscopic life in household dust A Barberán, RR Dunn, BJ Reich, K Pacifici, EB Laber, HL Menninger, ... Proceedings of the Royal Society B: Biological Sciences 282 (1814), 20151139, 2015 | 315 | 2015 |
| Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions B Zhang, AA Tsiatis, EB Laber, M Davidian Biometrika 100 (3), 681-694, 2013 | 276 | 2013 |
| Informing sequential clinical decision-making through reinforcement learning: an empirical study SM Shortreed, E Laber, DJ Lizotte, TS Stroup, J Pineau, SA Murphy Machine learning 84, 109-136, 2011 | 261 | 2011 |
| Tree-based methods for individualized treatment regimes EB Laber, YQ Zhao Biometrika 102 (3), 501-514, 2015 | 245 | 2015 |
| Dynamic treatment regimes: Technical challenges and applications EB Laber, DJ Lizotte, M Qian, WE Pelham, SA Murphy Electronic journal of statistics 8 (1), 1225, 2014 | 227 | 2014 |
| Dynamic treatment regimes: Statistical methods for precision medicine AA Tsiatis, M Davidian, ST Holloway, EB Laber Chapman and Hall/CRC, 2019 | 212 | 2019 |
| Doubly robust learning for estimating individualized treatment with censored data YQ Zhao, D Zeng, EB Laber, R Song, M Yuan, MR Kosorok Biometrika 102 (1), 151-168, 2015 | 197 | 2015 |
| Estimating dynamic treatment regimes in mobile health using v-learning DJ Luckett, EB Laber, AR Kahkoska, DM Maahs, E Mayer-Davis, ... Journal of the american statistical association, 2020 | 181 | 2020 |
| Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme B Chakraborty, EB Laber, Y Zhao Biometrics 69 (3), 714-723, 2013 | 152 | 2013 |
| Using decision lists to construct interpretable and parsimonious treatment regimes Y Zhang, EB Laber, A Tsiatis, M Davidian Biometrics 71 (4), 895-904, 2015 | 146 | 2015 |
| Interactive model building for Q-learning EB Laber, KA Linn, LA Stefanski Biometrika 101 (4), 831-847, 2014 | 116 | 2014 |
| Set‐valued dynamic treatment regimes for competing outcomes EB Laber, DJ Lizotte, B Ferguson Biometrics 70 (1), 53-61, 2014 | 99 | 2014 |
| Interpretable dynamic treatment regimes Y Zhang, EB Laber, M Davidian, AA Tsiatis Journal of the American Statistical Association 113 (524), 1541-1549, 2018 | 93 | 2018 |
| Adaptive confidence intervals for the test error in classification EB Laber, SA Murphy Journal of the American Statistical Association 106 (495), 904-913, 2011 | 82 | 2011 |
