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
322 2019 Assessing algorithmic fairness with unobserved protected class using data combination N Kallus, X Mao, A Zhou
Management Science 68 (3), 1959-1981, 2022
163 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
99 2019 Causal inference with noisy and missing covariates via matrix factorization N Kallus, X Mao, M Udell
Advances in neural information processing systems 31, 2018
76 2018 Causal inference under unmeasured confounding with negative controls: A minimax learning approach N Kallus, X Mao, M Uehara
arXiv preprint arXiv:2103.14029, 2021
59 2021 On the role of surrogates in the efficient estimation of treatment effects with limited outcome data N Kallus, X Mao
arXiv preprint arXiv:2003.12408, 2020
59 2020 Stochastic optimization forests N Kallus, X Mao
Management Science 69 (4), 1975-1994, 2023
52 2023 Fast rates for contextual linear optimization Y Hu, N Kallus, X Mao
Management Science 68 (6), 4236-4245, 2022
38 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
35 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
28 2022 Long-term causal inference under persistent confounding via data combination G Imbens, N Kallus, X Mao, Y Wang
arXiv preprint arXiv:2202.07234, 2022
24 2022 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
22 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
20 2019 Fast rates for contextual linear optimization Y Hu, N Kallus, X Mao
arXiv preprint arXiv:2011.03030, 2020
11 2020 Smooth contextual bandits: Bridging the parametric and nondifferentiable regret regimes Y Hu, N Kallus, X Mao
Operations Research 70 (6), 3261-3281, 2022
9 2022 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 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
7 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
6 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
2 2023 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
1 2024