| Minimax-optimal policy learning under unobserved confounding N Kallus, A Zhou Management Science 67 (5), 2870-2890, 2021 | 231* | 2021 |
| Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination N Kallus, X Mao, A Zhou Management Science, 2020 | 184 | 2020 |
| Residual Unfairness in Fair Machine Learning from Prejudiced Data N Kallus, A Zhou ICML 2018, 2018 | 165 | 2018 |
| Policy Evaluation and Optimization with Continuous Treatments N Kallus, A Zhou Proceedings of The 21st International Conference on Artificial Intelligence …, 2018 | 139 | 2018 |
| It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks M Bao, A Zhou, S Zottola, B Brubach, S Desmarais, A Horowitz, K Lum, ... Advances in Neural Information Processing Systems, Datasets and Benchmarks 2021, 2021 | 109 | 2021 |
| Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding N Kallus, X Mao, A Zhou Proceedings of The 22nd International Conference on Artificial Intelligence …, 2019 | 107 | 2019 |
| The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric N Kallus, A Zhou Advances in Neural Information Processing Systems, 3438-3448, 2019 | 79 | 2019 |
| Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning N Kallus, A Zhou Advances in Neural Information Processing Systems, 2020 | 64 | 2020 |
| Fairness, Welfare, and Equity in Personalized Pricing N Kallus, A Zhou ACM Conference on Fairness, Accountability, and Transparency, 2021 | 44 | 2021 |
| Participatory approaches to machine learning B Kulynych, D Madras, S Milli, ID Raji, A Zhou, R Zemel International Conference on Machine Learning Workshop, 2020 | 42 | 2020 |
| Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds N Kallus, A Zhou Advances in Neural Information Processing Systems, 3426-3437, 2019 | 26 | 2019 |
| Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders D Bruns-Smith, A Zhou https://arxiv.org/abs/2302.00662, 2023 | 11 | 2023 |
| Stateful Offline Contextual Policy Evaluation and Learning N Kallus, A Zhou Proceedings of The 25nd International Conference on Artificial Intelligence …, 2022 | 10 | 2022 |
| Data-Driven Influence Functions for Optimization-Based Causal Inference MI Jordan, Y Wang, A Zhou arXiv preprint arXiv:2208.13701, conference version "Empirical Gateaux …, 2022 | 8* | 2022 |
| An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controls A Zhou, A Koo, N Kallus, R Ropac, R Peterson, S Koppel, T Bergin Statistics and Public Policy, 2023 | 6 | 2023 |
| Off-Policy Evaluation with Policy-Dependent Optimization Response W Guo, MI Jordan, A Zhou Accepted at Neurips 2022, 2022 | 5 | 2022 |
| Reward-Relevance-Filtered Linear Offline Reinforcement Learning A Zhou https://arxiv.org/pdf/2401.12934.pdf, 2024 | 2 | 2024 |
| Optimal and Fair Encouragement Policy Evaluation and Learning A Zhou Neurips 2023, https://arxiv.org/abs/2309.07176, 2023 | 2 | 2023 |
| Optimizing and Learning Assortment Decisions in the Presence of Platform Disengagement M Sumida, A Zhou https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4537925, 2023 | 2 | 2023 |
| A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret C Lawless, A Zhou https://arxiv.org/abs/2211.05116, 2022 | 2 | 2022 |
