| Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence V Semenova, M Goldman, V Chernozhukov, M Taddy Quantitative Economics 14 (2), 471-510, 2023 | 263* | 2023 |
| Debiased machine learning of conditional average treatment effects and other causal functions V Semenova, V Chernozhukov Econometrics Journal, 2020 | 190* | 2020 |
| Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models D Nekipelov, V Semenova, V Syrgkanis Econometrics Journal, 2021 | 37* | 2021 |
| Generalized lee bounds V Semenova arXiv preprint arXiv:2008.12720, 2020 | 23* | 2020 |
| Debiased machine learning of set-identified linear models V Semenova Journal of Econometrics 235 (2), 1725-1746, 2023 | 12* | 2023 |
| Machine Learning for Dynamic Discrete Choice V Semenova arXiv preprint arXiv:1908.09173, 0 | 12* | |
| Adaptive estimation of intersection bounds: a classification approach V Semenova arXiv preprint arXiv:2303.00982, 2023 | 4 | 2023 |
| Essays in econometrics and machine learning V Semenova Massachusetts Institute of Technology, 2018 | 3 | 2018 |
| Inference on weighted average value function in high-dimensional state space V Chernozhukov, W Newey, V Semenova arXiv preprint arXiv:1908.09173, 2019 | 2 | 2019 |
| Supplement to ‘Inference on heterogeneous treatment effects in high-dimensional dynamic panels under weak dependence’ V Semenova, M Goldman, V Chernozhukov, M Taddy Quantitative Economics Supplemental Material 14, 2023 | 1 | 2023 |
| Causal Inference with Machine Learning V Chernozhukov, MD Cinelli, E Duflo, I Fernandez-Val, C Hansen, ... | | 2022 |
Victor ChernozhukovProfessor, Department of Economics + Center for Statistics and Data Science, MITVerified email at mit.edu
