| A boosting algorithm for estimating generalized propensity scores with continuous treatments Y Zhu, DL Coffman, D Ghosh Journal of causal inference 3 (1), 25-40, 2015 | 171 | 2015 |
| On estimating regression-based causal effects using sufficient dimension reduction W Luo, Y Zhu, D Ghosh Biometrika 104 (1), 51-65, 2017 | 60 | 2017 |
| Penalized regression procedures for variable selection in the potential outcomes framework D Ghosh, Y Zhu, DL Coffman Statistics in medicine 34 (10), 1645-1658, 2015 | 39 | 2015 |
| Variable selection for propensity score estimation via balancing covariates Y Zhu, M Schonbach, DL Coffman, JS Williams Epidemiology 26 (2), e14-e15, 2015 | 32 | 2015 |
| Reinforcement learning for energy-efficient trajectory design of UAVs AH Arani, MM Azari, P Hu, Y Zhu, H Yanikomeroglu, S Safavi-Naeini IEEE Internet of Things Journal 9 (11), 9060-9070, 2021 | 31 | 2021 |
| Fairness-aware link optimization for space-terrestrial integrated networks: A reinforcement learning framework AH Arani, P Hu, Y Zhu IEEE Access 9, 77624-77636, 2021 | 29 | 2021 |
| A model averaging approach for estimating propensity scores by optimizing balance Y Xie, Y Zhu, CA Cotton, P Wu Statistical methods in medical research 28 (1), 84-101, 2019 | 27 | 2019 |
| Matching using sufficient dimension reduction for causal inference W Luo, Y Zhu Journal of Business & Economic Statistics 38 (4), 888-900, 2020 | 25 | 2020 |
| Re-envisioning space-air-ground integrated networks: Reinforcement learning for link optimization AH Arani, P Hu, Y Zhu ICC 2021-IEEE International Conference on Communications, 1-7, 2021 | 24 | 2021 |
| An anomaly detection method for satellites using Monte Carlo dropout MAM Sadr, Y Zhu, P Hu IEEE Transactions on Aerospace and Electronic Systems 59 (2), 2044-2052, 2022 | 20 | 2022 |
| Variable selection for causal mediation analysis using LASSO-based methods Z Ye, Y Zhu, DL Coffman Statistical methods in medical research 30 (6), 1413-1427, 2021 | 18 | 2021 |
| A Kernel-Based Metric for Balance Assessment Y Zhu, JS Savage, D Ghosh Journal of Causal Inference, 2018 | 18 | 2018 |
| Learning heterogeneity in causal inference using sufficient dimension reduction W Luo, W Wu, Y Zhu Journal of Causal Inference 7 (1), 20180015, 2019 | 16 | 2019 |
| A data-adaptive strategy for inverse weighted estimation of causal effects Y Zhu, D Ghosh, N Mitra, B Mukherjee Health Services and Outcomes Research Methodology 14, 69-91, 2014 | 16 | 2014 |
| HAPS-UAV-enabled heterogeneous networks: A deep reinforcement learning approach AH Arani, P Hu, Y Zhu IEEE Open Journal of the Communications Society, 2023 | 12 | 2023 |
| Multiply robust estimation of causal quantile treatment effects Y Xie, C Cotton, Y Zhu Statistics in Medicine 39 (28), 4238-4251, 2020 | 12 | 2020 |
| Using conventional and machine learning propensity score methods to examine the effectiveness of 12-step group involvement following inpatient addiction treatment MJ Costello, Y Li, Y Zhu, A Walji, S Sousa, S Remers, Y Chorny, B Rush, ... Drug and Alcohol Dependence 227, 108943, 2021 | 11 | 2021 |
| Causal inference for multi-level treatments with machine-learned propensity scores L Lin, Y Zhu, L Chen Health Services and Outcomes Research Methodology 19, 106-126, 2019 | 9 | 2019 |
| A comparison of potential outcome approaches for assessing causal mediation DL Coffman, DP MacKinnon, Y Zhu, D Ghosh Statistical causal inferences and their applications in public health …, 2016 | 9 | 2016 |
| High-dimensional causal mediation analysis based on partial linear structural equation models X Cai, Y Zhu, Y Huang, D Ghosh Computational Statistics & Data Analysis 174, 107501, 2022 | 8 | 2022 |
