| From predictive to prescriptive analytics D Bertsimas, N Kallus Management Science 66 (3), 1025-1044, 2020 | 1135 | 2020 |
| Data-driven robust optimization D Bertsimas, V Gupta, N Kallus Mathematical Programming 167 (2), 235-292, 2018 | 924 | 2018 |
| Fairness under unawareness: Assessing disparity when protected class is unobserved J Chen, N Kallus, X Mao, G Svacha, M Udell Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 441 | 2019 |
| Robust sample average approximation D Bertsimas, V Gupta, N Kallus Mathematical Programming 171 (1), 217-282, 2018 | 357 | 2018 |
| Balanced policy evaluation and learning N Kallus Advances in neural information processing systems 31, 2018 | 336 | 2018 |
| Large language models as zero-shot conversational recommenders Z He, Z Xie, R Jha, H Steck, D Liang, Y Feng, BP Majumder, N Kallus, ... Proceedings of the 32nd ACM international conference on information and …, 2023 | 304 | 2023 |
| Double reinforcement learning for efficient off-policy evaluation in markov decision processes N Kallus, M Uehara Journal of Machine Learning Research 21 (167), 1-63, 2020 | 266 | 2020 |
| Is cosine-similarity of embeddings really about similarity? H Steck, C Ekanadham, N Kallus Companion Proceedings of the ACM Web Conference 2024, 887-890, 2024 | 239 | 2024 |
| Assessing algorithmic fairness with unobserved protected class using data combination N Kallus, X Mao, A Zhou Management Science 68 (3), 1959-1981, 2022 | 237 | 2022 |
| Confounding-robust policy improvement N Kallus, A Zhou Advances in neural information processing systems 31, 2018 | 224 | 2018 |
| Policy evaluation and optimization with continuous treatments N Kallus, A Zhou International conference on artificial intelligence and statistics, 1243-1251, 2018 | 202 | 2018 |
| Removing hidden confounding by experimental grounding N Kallus, AM Puli, U Shalit Advances in neural information processing systems 31, 2018 | 195 | 2018 |
| Deep generalized method of moments for instrumental variable analysis A Bennett, N Kallus, T Schnabel Advances in neural information processing systems 32, 2019 | 194 | 2019 |
| Residual unfairness in fair machine learning from prejudiced data N Kallus, A Zhou International Conference on Machine Learning, 2439-2448, 2018 | 192 | 2018 |
| Personalized diabetes management using electronic medical records D Bertsimas, N Kallus, AM Weinstein, YD Zhuo Diabetes care 40 (2), 210-217, 2017 | 179 | 2017 |
| Generalization bounds and representation learning for estimation of potential outcomes and causal effects FD Johansson, U Shalit, N Kallus, D Sontag Journal of Machine Learning Research 23 (166), 1-50, 2022 | 170 | 2022 |
| Generalized optimal matching methods for causal inference. N Kallus J. Mach. Learn. Res. 21, 62:1-62:54, 2020 | 166 | 2020 |
| Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning N Kallus, M Uehara Operations Research 70 (6), 3035-3628, 2022 | 157* | 2022 |
| Predicting crowd behavior with big public data N Kallus Proceedings of the 23rd International Conference on World Wide Web, 625-630, 2014 | 146 | 2014 |
| A review of off-policy evaluation in reinforcement learning M Uehara, C Shi, N Kallus arXiv preprint arXiv:2212.06355, 2022 | 141 | 2022 |
