Bio

Zijun Gao is a Ph.D. candidate in the Statistics Department at Stanford University advised by Professor Trevor Hastie. Prior to attending Stanford, she obtained a Bachelor of Science in Mathematics from Tsinghua University, China.

Her major research interest is causal inference with heterogeneity. Her works focus on developing efficient methodologies of estimating and validating heterogeneous causal effects with applications of large-scale healthcare databases. She also works on real-world data motivated topics such as conditional density estimation and batched bandit problem.

Honors & Awards

  • Ric Weiland Fellowship, Ric Weiland (Oct. 2020 - Present)
  • Outstanding Undergraduates in Tsinghua, Tsinghua University (Jul. 2017)
  • Group gold medal in 4th Romanian Master of Mathematics, Romanian Mathematical Society (Feb. 2012)

Professional Affiliations and Activities

  • Reviewer, Neural Information Processing Systems (NeurIPS) (2020 - Present)
  • Reviewer, International Conference on Learning Representations (ICLR) (2021 - Present)

Personal Interests

Piano, Painting, Running

Contact

Additional Info

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Current Research and Scholarly Interests

Causal inference, density estimation, optimization

All Publications

  • Assessment of heterogeneous treatment effect estimation accuracy via matching. Statistics in medicine Gao, Z., Hastie, T., Tibshirani, R. 2021

    Abstract

    We study the assessment of the accuracy of heterogeneous treatment effect (HTE) estimation, where the HTE is not directly observable so standard computation of prediction errors is not applicable. To tackle the difficulty, we propose an assessment approach by constructing pseudo-observations of the HTE based on matching. Our contributions are three-fold: first, we introduce a novel matching distance derived from proximity scores in random forests; second, we formulate the matching problem as an average minimum-cost flow problem and provide an efficient algorithm; third, we propose a match-then-split principle for the assessment with cross-validation. We demonstrate the efficacy of the assessment approach using simulations and a real dataset.

    View details for DOI 10.1002/sim.9010

    View details for PubMedID 33915600

  • Perioperative analgesic administration during the 2018 parenteral opioid shortage in the United States - A retrospective analysis. Journal of clinical anesthesia Kim, R. K., Gao, Z. n., Hastie, T. n., Obal, D. n. 2020; 66: 109892

    View details for DOI 10.1016/j.jclinane.2020.109892

    View details for PubMedID 32502773

  • Minimax optimal nonparametric estimation of heterogeneous treatment effects Conference on Neural Information Processing Systems (NeurIPS) Gao, Z., Han, Y. 2020: 12
  • Batched Multi-armed Bandits Problem Gao, Z., Han, Y., Ren, Z., Zhou, Z., Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000534424300046

https://orcid.org/0000-0003-4863-1656