EconBase
← All authors

Xiaojie Mao

Tsinghua University (from arXiv:2302.05404, 2023) · ORCID · OpenAlex

11 papers in scope · 8 published · 6 on the econ.EM arXiv · 123 citations · h-index 6 (over the papers listed here)

Related authors

The 20 authors closest to this one in our weighted citation graph, most related first.

  1. Nathan Kallus
  2. Andrew Bennett
  3. Vasilis Syrgkanis
  4. Masatoshi Uehara
  5. Rahul Singh
  6. Whitney K. Newey
  7. Yuhao Wang
  8. Nishanth Dikkala
  9. Greg Lewis
  10. Ting-Chih Hung
  11. Yu-Chang Chen
  12. Lester Mackey
  13. Tobias Schnabel
  14. AmirEmad Ghassami
  15. Guido W. Imbens
  16. Ben Deaner
  17. Jiawei Fu
  18. Donald P. Green
  19. Si Kai Lee
  20. Anant Raj

Proximity is measured over citations between two papers we both hold, weighted by how heavily one leans on the other, and is symmetric — it does not distinguish citing from being cited. Authors without a profile here are skipped, and a genuinely close colleague can be missing simply because their work is not in our arXiv corpus. Method: docs/06-citations-pipeline.md.

Papers

(5 of 11)

Online Joint Assortment-Inventory Optimization Under MNL Choices
published2026 · Operations Research · first circulated 2023
with Yong Liang, Shiyuan Wang
Operations Research
What, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning
published2025 · Information Systems Research · 5 citations · first circulated 2024
with Bowen Shi, Mochen Yang, Bo Li
Information Systems Research
Inference on strongly identified functionals of weakly identified functions
published2025 · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 2 citations · first circulated 2022
with Andrew Bennett, Nathan Kallus, Whitney K. Newey, Vasilis Syrgkanis, Masatoshi Uehara, Whitney Newey
Paper profile Journal of the Royal Statistical Society Series B (Statistical Methodology) arXiv:2208.08291
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
published2024 · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 19 citations · first circulated 2020
with Nathan Kallus
Journal of the Royal Statistical Society Series B (Statistical Methodology)
Long-term causal inference under persistent confounding via data combination
published2024 · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 10 citations · first circulated 2022
with Guido W. Imbens, Nathan Kallus, Yuhao Wang
Paper profile Journal of the Royal Statistical Society Series B (Statistical Methodology) arXiv:2202.07234
Source Condition Double Robust Inference on Functionals of Inverse Problems
working paper2023 · arXiv · 1 citations
with Andrew F. Bennett, Nathan Kallus, Whitney K. Newey, Vasilis Syrgkanis, Masatoshi Uehara
Paper profile arXiv arXiv:2307.13793
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness
working paper2023 · arXiv
with Andrew F. Bennett, Nathan Kallus, Whitney K. Newey, Vasilis Syrgkanis, Masatoshi Uehara
Paper profile arXiv arXiv:2302.05404
Fast Rates for Contextual Linear Optimization
published2022 · Management Science · 35 citations · first circulated 2020
with Yichun Hu, Nathan Kallus
Management Science
Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes
published2022 · Operations Research · 20 citations · first circulated 2019
with Yichun Hu, Nathan Kallus
Operations Research
Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models
working paper2021 · arXiv · 6 citations
with Guido W. Imbens, Nathan Kallus
Paper profile arXiv arXiv:2108.03849
Causal Inference with Noisy and Missing Covariates via Matrix Factorization
published2018 · Neural Information Processing Systems · 25 citations
with Nathan Kallus, Madeleine Udell
Neural Information Processing Systems

Assembled from arXiv and OpenAlex. Duplicate records for the same paper are merged, and the published version is shown where we could identify one. Corrections welcome.