2021.07 ~ present  Assistant Professor, Department of Management Science and Engineering, Tsinghua University 

Empirical Methods in Management Science (PhD)

Data Analytics: Inference and Decision Making (Master)

Probability Theory and Mathematical Statistics (Undergraduate)

Causal Inference, Data-driven Decision Making

Manuscripts and Code can be found in https://xiaojiemao.github.io/.

Publications

• 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. 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. 
  

• 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.

Working Papers

• 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.

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