| From predictive to prescriptive analytics D Bertsimas, N Kallus Management Science 66 (3), 1025-1044, 2020 | 815 | 2020 |
| Data-driven robust optimization D Bertsimas, V Gupta, N Kallus Mathematical Programming 167, 235-292, 2018 | 776 | 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 | 365 | 2019 |
| Balanced policy evaluation and learning N Kallus Advances in neural information processing systems 31, 2018 | 302 | 2018 |
| Robust sample average approximation D Bertsimas, V Gupta, N Kallus Mathematical Programming 171 (1), 217-282, 2018 | 297 | 2018 |
| 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 | 215 | 2020 |
| Assessing algorithmic fairness with unobserved protected class using data combination N Kallus, X Mao, A Zhou Management Science 68 (3), 1959-1981, 2022 | 193 | 2022 |
| Confounding-robust policy improvement N Kallus, A Zhou Advances in neural information processing systems 31, 2018 | 191 | 2018 |
| Residual unfairness in fair machine learning from prejudiced data N Kallus, A Zhou International Conference on Machine Learning, 2439-2448, 2018 | 167 | 2018 |
| Personalized diabetes management using electronic medical records D Bertsimas, N Kallus, AM Weinstein, YD Zhuo Diabetes care 40 (2), 210-217, 2017 | 159 | 2017 |
| Deep generalized method of moments for instrumental variable analysis A Bennett, N Kallus, T Schnabel Advances in neural information processing systems 32, 2019 | 152 | 2019 |
| Policy evaluation and optimization with continuous treatments N Kallus, A Zhou International conference on artificial intelligence and statistics, 1243-1251, 2018 | 148 | 2018 |
| Removing hidden confounding by experimental grounding N Kallus, AM Puli, U Shalit Advances in neural information processing systems 31, 2018 | 145 | 2018 |
| Generalized optimal matching methods for causal inference. N Kallus J. Mach. Learn. Res. 21, 62:1-62:54, 2020 | 140 | 2020 |
| Predicting crowd behavior with big public data N Kallus Proceedings of the 23rd International Conference on World Wide Web, 625-630, 2014 | 137 | 2014 |
| 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 | 131 | 2023 |
| Recursive partitioning for personalization using observational data N Kallus International conference on machine learning, 1789-1798, 2017 | 126* | 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 | 121 | 2022 |
| Interval estimation of individual-level causal effects under unobserved confounding N Kallus, X Mao, A Zhou The 22nd international conference on artificial intelligence and statistics …, 2019 | 116 | 2019 |
| Optimal A Priori Balance in the Design of Controlled Experiments N Kallus Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2018 | 112 | 2018 |
