| Lasso adjustments of treatment effect estimates in randomized experiments A Bloniarz, H Liu, CH Zhang, JS Sekhon, B Yu Proceedings of the National Academy of Sciences 113 (27), 7383-7390, 2016 | 201 | 2016 |
| Asymptotic properties of Lasso+ mLS and Lasso+ Ridge in sparse high-dimensional linear regression H Liu, B Yu | 115 | 2013 |
| Nonnegative-lasso and application in index tracking L Wu, Y Yang, H Liu Computational Statistics & Data Analysis 70, 116-126, 2014 | 109 | 2014 |
| Regression-adjusted average treatment effect estimates in stratified randomized experiments H Liu, Y Yang Biometrika 107 (4), 935-948, 2020 | 45 | 2020 |
| A bootstrap lasso+ partial ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models H Liu, X Xu, JJ Li Statistica Sinica 30, 1333-1355, 2020 | 35 | 2020 |
| Regression analysis for covariate‐adaptive randomization: A robust and efficient inference perspective W Ma, F Tu, H Liu Statistics in Medicine 41 (29), 5645-5661, 2022 | 24 | 2022 |
| Rerandomization in stratified randomized experiments X Wang, T Wang, H Liu Journal of the American Statistical Association, 1-10, 2021 | 22 | 2021 |
| Lasso-adjusted treatment effect estimation under covariate-adaptive randomization H Liu, F Tu, W Ma Biometrika 110 (2), 431-447, 2023 | 11 | 2023 |
| Design-based theory for cluster rerandomization X Lu, T Liu, H Liu, P Ding Biometrika in press, 2022 | 11 | 2022 |
| Randomization-based Joint Central Limit Theorem and Efficient Covariate Adjustment in Randomized Block 2K Factorial Experiments H Liu, J Ren, Y Yang Journal of the American Statistical Association 119 (545), 136-150, 2024 | 8 | 2024 |
| A general theory of regression adjustment for covariate-adaptive randomization: OLS, Lasso, and beyond H Liu, F Tu, W Ma arXiv preprint arXiv:2011.09734, 2020 | 7 | 2020 |
| Heterogeneous treatment effect estimation through deep learning R Chen, H Liu arXiv preprint arXiv:1810.11010, 2018 | 7 | 2018 |
| Pair-switching rerandomization K Zhu, H Liu biometrics, accepted, 2021 | 5 | 2021 |
| Comments on: High-dimensional simultaneous inference with the bootstrap H Liu, B Yu Test 26, 740-750, 2017 | 5 | 2017 |
| Confidence intervals for parameters in high-dimensional sparse vector autoregression K Zhu, H Liu Computational Statistics and Data Analysis, accepted, 2020 | 4 | 2020 |
| Penalized regression adjusted causal effect estimates in high dimensional randomized experiments H Liu, Y Yang arXiv preprint arXiv:1809.08732, 2018 | 4 | 2018 |
| Rerandomization and covariate adjustment in split-plot designs W Shi, A Zhao, H Liu arXiv preprint arXiv:2209.12385, 2022 | 2 | 2022 |
| Blocking, rerandomization, and regression adjustment in randomized experiments with high-dimensional covariates K Zhu, H Liu, Y Yang arXiv preprint arXiv:2109.11271, 2021 | 2 | 2021 |
| Tyranny-of-the-minority regression adjustment in randomized experiments X Lu, H Liu arXiv preprint arXiv:2210.00261, 2022 | 1 | 2022 |
| Design-based theory for Lasso adjustment in randomized block experiments with a general blocking scheme K Zhu, H Liu, Y Yang arXiv preprint arXiv:2109.11271, 2021 | 1 | 2021 |
