| 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 | 375 | 2019 |
| Assessing algorithmic fairness with unobserved protected class using data combination N Kallus, X Mao, A Zhou Management Science 68 (3), 1959-1981, 2022 | 200 | 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 | 117 | 2019 |
| On the role of surrogates in the efficient estimation of treatment effects with limited outcome data N Kallus, X Mao Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2024 | 82 | 2024 |
| Causal inference with noisy and missing covariates via matrix factorization N Kallus, X Mao, M Udell Advances in neural information processing systems 31, 2018 | 81 | 2018 |
| Stochastic optimization forests N Kallus, X Mao Management Science 69 (4), 1975-1994, 2023 | 72 | 2023 |
| Causal inference under unmeasured confounding with negative controls: A minimax learning approach N Kallus, X Mao, M Uehara arXiv preprint arXiv:2103.14029, 2021 | 71 | 2021 |
| Fast rates for contextual linear optimization Y Hu, N Kallus, X Mao Management Science 68 (6), 4236-4245, 2022 | 54 | 2022 |
| Smooth contextual bandits: Bridging the parametric and non-differentiable regret regimes Y Hu, N Kallus, X Mao Conference on Learning Theory, 2007-2010, 2020 | 53 | 2020 |
| Doubly robust distributionally robust off-policy evaluation and learning N Kallus, X Mao, K Wang, Z Zhou International Conference on Machine Learning, 10598-10632, 2022 | 42 | 2022 |
| Long-term causal inference under persistent confounding via data combination G Imbens, N Kallus, X Mao, Y Wang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2024 | 39 | 2024 |
| Controlling for unmeasured confounding in panel data using minimal bridge functions: From two-way fixed effects to factor models G Imbens, N Kallus, X Mao arXiv preprint arXiv:2108.03849, 2021 | 27 | 2021 |
| Localized debiased machine learning: Efficient inference on quantile treatment effects and beyond N Kallus, X Mao, M Uehara arXiv preprint arXiv:1912.12945, 2019 | 23 | 2019 |
| Minimax Instrumental Variable Regression and Convergence Guarantees without Identification or Closedness A Bennett, N Kallus, X Mao, W Newey, V Syrgkanis, M Uehara The Thirty Sixth Annual Conference on Learning Theory, 2291-2318, 2023 | 20 | 2023 |
| Inference on strongly identified functionals of weakly identified functions A Bennett, N Kallus, X Mao, W Newey, V Syrgkanis, M Uehara arXiv preprint arXiv:2208.08291, 2022 | 13 | 2022 |
| Source condition double robust inference on functionals of inverse problems A Bennett, N Kallus, X Mao, W Newey, V Syrgkanis, M Uehara arXiv preprint arXiv:2307.13793, 2023 | 11 | 2023 |
| Fast rates for contextual linear optimization Y Hu, N Kallus, X Mao arXiv preprint arXiv:2011.03030, 2020 | 11 | 2020 |
| Localized debiased machine learning: Efficient inference on quantile treatment effects and beyond N Kallus, X Mao, M Uehara Journal of Machine Learning Research 25 (16), 1-59, 2024 | 10 | 2024 |
| Localized debiased machine learning: Efficient estimation of quantile treatment effects, conditional value at risk, and beyond N Kallus, X Mao, M Uehara stat 1050, 30, 2019 | 8 | 2019 |
| What, Why, and How: An Empiricist's Guide to Double/Debiased Machine Learning B Shi, X Mao, M Yang, B Li Debiased Machine Learning (December 27, 2023), 2023 | 2 | 2023 |
Nathan KallusCornell UniversityVerificeret mail på cornell.edu