| Birth month affects lifetime disease risk: a phenome-wide method MR Boland, Z Shahn, D Madigan, G Hripcsak, NP Tatonetti Journal of the American Medical Informatics Association 22 (5), 1042-1053, 2015 | 147 | 2015 |
| Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients MY Lu, Z Shahn, D Sow, F Doshi-Velez, L Lehman AMIA Annual Symposium Proceedings 2020, 773-782, 2021 | 34* | 2021 |
| Door-to-needle delays in minor stroke: a causal inference approach SK Rostanski, Z Shahn, MSV Elkind, AL Liberman, RS Marshall, ... Stroke 48 (7), 1980-1982, 2017 | 24 | 2017 |
| G-net: a recurrent network approach to g-computation for counterfactual prediction under a dynamic treatment regime R Li, S Hu, M Lu, Y Utsumi, P Chakraborty, DM Sow, P Madan, J Li, ... Machine Learning for Health, 282-299, 2021 | 22 | 2021 |
| Fluid-limiting treatment strategies among sepsis patients in the ICU: a retrospective causal analysis Z Shahn, NI Shapiro, PD Tyler, D Talmor, LH Lehman Critical Care 24, 1-9, 2020 | 21 | 2020 |
| Predicting and understanding unexpected respiratory decompensation in critical care using sparse and heterogeneous clinical data O Ren, AEW Johnson, EP Lehman, M Komorowski, J Aboab, F Tang, ... 2018 IEEE International Conference on Healthcare Informatics (ICHI), 144-151, 2018 | 21 | 2018 |
| Latent class mixture models of treatment effect heterogeneity Z Shahn, D Madigan | 19 | 2017 |
| Predicting health outcomes from high‐dimensional longitudinal health histories using relational random forests Z Shahn, P Ryan, D Madigan Statistical Analysis and Data Mining: the ASA Data Science Journal 8 (2 …, 2015 | 11 | 2015 |
| A formal causal interpretation of the case‐crossover design Z Shahn, MA Hernán, JM Robins Biometrics 79 (2), 1330-1343, 2023 | 8 | 2023 |
| Structural nested mean models under parallel trends assumptions Z Shahn, O Dukes, D Richardson, ET Tchetgen, J Robins arXiv preprint arXiv:2204.10291, 2022 | 8 | 2022 |
| Efficient estimation of optimal regimes under a no direct effect assumption L Liu, Z Shahn, JM Robins, A Rotnitzky Journal of the American Statistical Association 116 (533), 224-239, 2021 | 7 | 2021 |
| G-computation and hierarchical models for estimating multiple causal effects from observational disease registries with irregular visits Z Shahn, Y Li, Z Sun, A Mohan, C Sampaio, J Hu AMIA Summits on Translational Science Proceedings 2019, 789, 2019 | 7 | 2019 |
| Trends in control of unobserved confounding Z Shahn Epidemiology 28 (4), 537-539, 2017 | 6 | 2017 |
| Titration of ventilator settings to target driving pressure and mechanical power ENB Kassis, S Hu, MY Lu, AEW Johnson, S Bose, MS Schaefer, ... Respiratory Care 68 (2), 199-207, 2023 | 5 | 2023 |
| Semiparametric bespoke instrumental variables O Dukes, D Richardson, Z Shahn, ET Tchetgen arXiv preprint arXiv:2204.04119, 2022 | 5 | 2022 |
| Delaying initiation of diuretics in critically ill patients with recent vasopressor use and high positive fluid balance Z Shahn, LWH Lehman, RG Mark, D Talmor, S Bose British Journal of Anaesthesia 127 (4), 569-576, 2021 | 4 | 2021 |
| Estimating optimal dynamic treatment strategies under resource constraints using dynamic marginal structural models E Caniglia, E Murray, M Hernán, Z Shahn Statistics in Medicine 40 (23), 4996-5005, 2021 | 4 | 2021 |
| Effects of aggressive and conservative strategies for mechanical ventilation liberation Z Shahn, A Choudhri, B Jung, D Talmor, HL Li-wei, E Baedorf-Kassis Journal of Critical Care 76, 154275, 2023 | 3 | 2023 |
| Bias formulas for violations of proximal identification assumptions R Cobzaru, R Welsch, S Finkelstein, K Ng, Z Shahn arXiv preprint arXiv:2208.00105, 2022 | 2 | 2022 |
| Subgroup difference in differences to identify effect modification without a control group Z Shahn arXiv preprint arXiv:2306.11030, 2023 | 1 | 2023 |
