| Metalearners for estimating heterogeneous treatment effects using machine learning SR Künzel, JS Sekhon, PJ Bickel, B Yu Proceedings of the national academy of sciences 116 (10), 4156-4165, 2019 | 1164 | 2019 |
| Transfer learning for estimating causal effects using neural networks SR Künzel, BC Stadie, N Vemuri, V Ramakrishnan, JS Sekhon, P Abbeel arXiv preprint arXiv:1808.07804, 2018 | 36 | 2018 |
| Causaltoolbox—estimator stability for heterogeneous treatment effects SR Künzel, SJS Walter, JS Sekhon Observational Studies 5 (2), 105-117, 2019 | 18 | 2019 |
| A comprehensive statistical description of radio-through-γ-ray spectral energy distributions of all known blazars P Mao, CM Urry, F Massaro, A Paggi, J Cauteruccio, SR Künzel The Astrophysical Journal Supplement Series 224 (2), 26, 2016 | 14 | 2016 |
| Linear aggregation in tree-based estimators SR Künzel, TF Saarinen, EW Liu, JS Sekhon Journal of Computational and Graphical Statistics 31 (3), 917-934, 2022 | 9 | 2022 |
| Heterogeneous Treatment Effect Estimation Using Machine Learning SR Kuenzel University of California, Berkeley, 2019 | 7 | 2019 |
| Estimating heterogeneous treatment effects using neural networks with the Y-Learner BC Stadie, SR Künzel, N Vemuri, JS Sekhon | 3 | 2018 |
| Remarks on Kneip's linear smoothers SR Künzel, D Pollard, D Yang arXiv preprint arXiv:1405.1744, 2014 | 1 | 2014 |
Jasjeet SekhonEugene Meyer Professor of Data Science, Political Science, and Statistics, Yale University在 yale.edu 的电子邮件经过验证
