Nathan Kallus, Xiaojie Mao, Masatoshi Uehara. Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects and Beyond. Journal of Machine Learning Research, 2024. (中国计算机学会A类期刊)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara. Inference on Strongly Identified Functionals of Weakly Identified Functions. Conference on Learning Theory, 2023.
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara. Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. Conference on Learning Theory, 2023.
Nathan Kallus, Xiaojie Mao, Kaiwen Wang, Zhengyuan Zhou (2022). Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning. International Conference on Machine Learning, 2022. (中国计算机学会A类会议)
Nathan Kallus, Xiaojie Mao. Stochastic Optimization Forests. Management Science, 2022. (UTD 24期刊)
Yichun Hu, Nathan Kallus, Xiaojie Mao. Fast Rates for Contextual Linear Optimization. Management Science (Fast Track), 2022. (UTD 24期刊)
Yichun Hu, Nathan Kallus, Xiaojie Mao. Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. Operations Research, 2021. (UTD 24期刊，论文获得Finalist for Applied Probability Society 2020 Best Student Paper Competition).
Nathan Kallus, Xiaojie Mao, Angela Zhou. Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination. Management Science Special Section on Data-Driven Prescriptive Analytics, 2022. (UTD 24期刊, Featured Article in Management Science Vol 68 Issue 3 with invited review at https://www.informs.org/Blogs/ManSci-Blogs/Management-Science-Review/If-You-Can-t-Measure-It-Bound-It-Credibly-Auditing-Algorithms-for-Fairness2).
Nathan Kallus, Xiaojie Mao, Angela Zhou. Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding. The 22nd International Conference on Artificial Intelligence and Statistics, 2019.
Jiahao Chen, Nathan Kallus, Xiaojie Mao, Geoffry Svacha, Madeleine Udell. Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved. ACM FAT* 2019: Conference on Fairness, Accountability, and Transparency in Machine Learning.
Nathan Kallus, Xiaojie Mao, Madeleine Udell. Causal Inference with Noisy and Missing Covariates via Matrix Factorization. The 32nd Annual Conference on Neural Information Processing Systems, 2018. (中国计算机学会A类会议)
Yong Liang, Xiaojie Mao, Shiyuan Wang. Online Joint Assortment-Inventory Optimization under MNL Choices. arxiv preprint 2304.02022.
Guido Imbens, Nathan Kallus, Xiaojie Mao, Yuhao Wang. Long-term causal inference under persistent confounding via data combination. arxiv preprint 2202.07234.
Guido Imbens, Nathan Kallus, Xiaojie Mao. Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models. arxiv preprint 2108.03849.
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara. Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. arxiv preprint 2103.14029.
Nathan Kallus, Xiaojie Mao. On the role of surrogates in the efficient estimation of treatment effects with limited outcome data. arxiv preprint 2003.12408.
