2024.07 ~ present  Associate Professor (untenured), Department of Management Science and Engineering, Tsinghua University 

2021.07 ~ 2024.07  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

Google Scholar: https://scholar.google.com/citations?user=XtSSJm0AAAAJ&hl=en&oi=ao

Publications

• 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. 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.

無

無

無