Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara. Inference on Strongly Identified Functionals of Weakly Identified Functions. Accepted by Journal of the Royal Statistical Society Series B, 2025 (Preliminary Version Accepted by COLT 2023).
Bowen Shi, Xiaojie Mao, Mochen Yang, Bo Li. What, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning. Accepted by Information Systems Research, 2025.
Tianrun Zhao, Xiaojie Mao, Yong Liang. Online Strategic Classification with Noise and Partial Feedback. Annual Conference on Neural Information Processing Systems (Spotlight, 3%), 2025.
Zhiyi Li, Xiaojie Mao, Yunbei Xu, Ruohan Zhan. Statistical Properties of Robust Optimization under Distribution Shifts. NeurIPS 2025 Workshop ML×OR.
Jian Chen, Zhehao Li, Xiaojie Mao. Learning under Selective Labels with Data from Heterogeneous Decision-makers: An Instrumental Variable Approach. International Conference on Machine Learning, 2025.
Yichun Hu, Nathan Kallus, Xiaojie Mao, Yanchen Wu. Contextual Linear Optimization with Bandit Feedback. The 38th Annual Conference on Neural Information Processing Systems, 2024.
Guido Imbens, Nathan Kallus, Xiaojie Mao, Yuhao Wang. Long-term causal inference under persistent confounding via data combination. Accepted by Journal of the Royal Statistical Society Series B.
Nathan Kallus, Xiaojie Mao. On the Role of Surrogates in the Efficient Estimation of Treatment Effects with Limited Outcome Data. Accepted by Journal of the Royal Statistical Society Series B.
Nathan Kallus, Xiaojie Mao, Masatoshi Uehara. Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects and Beyond. Forthcoming in the Journal of Machine Learning Research, 2024.
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.
Nathan Kallus, Xiaojie Mao. Stochastic Optimization Forests. Management Science, 2022.
Yichun Hu, Nathan Kallus, Xiaojie Mao. Fast Rates for Contextual Linear Optimization. Management Science, 2022.
Yichun Hu, Nathan Kallus, Xiaojie Mao. Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes. Operations Research, 2022. 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 Issue on Data-Driven Prescriptive Analytics, 2021. Preliminary Version Accepted in FAT* 2020 and NeurIPS 2019 Workshop on Fair ML for Health.
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.
