Ke Zhu

Ke Zhu

Postdoctoral Research Scholar in Statistics

I’m Ke Zhu, a Postdoctoral Research Scholar jointly affiliated with North Carolina State University and Duke University, mentored by Dr. Shu Yang and Dr. Xiaofei Wang. I received my PhD from Tsinghua University, where I was advised by Dr. Hanzhong Liu.

My research focuses on developing statistical methodology and theory for causal inference, with an emphasis on randomization-based inference, high-dimensional statistics, and data fusion. I aim to build robust and efficient methods for complex experimental and observational studies by integrating uncertainty-aware machine learning and heterogeneous multi-source data. Motivated by real-world challenges, my work is closely tied to applications in oncology clinical trials, labor economics, and public policy.

Education

  • Ph.D. in Statistics, Tsinghua University, 2023
  • B.A. in Economics, Peking University, 2018
  • B.S. in Statistics, Beijing Normal University, 2017

Publications

(† indicates co-first author; * indicates corresponding author)

[1] Yu, H., Zhu, K.*, & Liu, H. (2025). Sharp variance estimator and causal bootstrap in stratified randomized experiments. Statistics in Medicine, 44(13-14): e70139. [Arxiv] [Package]

[2] Zhu, K.†, Chu, J.†, Lipkovich, I., Ye, W., & Yang, S.* (2025). Doubly robust fusion of many treatments for policy learning. Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR, in press. [Poster]

[3] Zhu, K., Yang, S.*, & Wang, X. (2025). Enhancing statistical validity and power in hybrid controlled trials: A randomization inference approach with conformal selective borrowing. Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR, in press. [Slides] [Poster] [Package] [Code]

** Winner of the Postdoc Presentation Award at the 2025 NISS Virtual New Researchers Conference

[4] Zhu, K., Liu, H.*, & Yang, Y.* (2025). Design-based theory for lasso adjustment in randomized block experiments and rerandomized experiments. Journal of Business & Economic Statistics, 43(3): 544-555. [Paper] [Slides] [Code]

[5] Zhu, K. & Liu, H.* (2024). Rejoinder to reader reaction “On exact randomization-based covariate-adjusted confidence intervals” by Jacob Fiksel. Biometrics, 80(2): ujae052. [Paper] [Code]

[6] Zhu, K. & Liu, H.* (2023). Pair-switching rerandomization. Biometrics, 79(3): 2127-2142. [Paper] [Code]

[7] Zhu, K. & Liu, H.* (2022). Confidence intervals for parameters in high-dimensional sparse vector autoregression. Computational Statistics & Data Analysis, 168: 107383. [Paper]

Submitted Papers

[8] Liu, J.†, Zhu, K.†, Yang, S., & Wang, X.* (2025). Robust estimation and inference in hybrid controlled trials for binary outcomes: A case study on non-small cell lung cancer. Submitted.

[9] Han, T.†, Zhu, K.†, Liu, H.*, & Deng, K.* (2024). Imputation-based randomization tests for randomized experiments with interference. Submitted. [Package]

[10] Liu, Y., Levis, A. W., Zhu, K., Yang, S., Gilbert, P. B., & Han, L.* (2025). Targeted data fusion for causal survival analysis under distribution shift. Submitted.

[11] Liu, Y., Zhu, K., Han, L., & Yang, S.* (2025). COADVISE: Covariate adjustment with variable selection in randomized controlled trials. Submitted. [Package]

Interdisciplinary Collaborations

[12] Xia, F., Zhu, K., Ren, Y., & Wang, N.* (2024). Efficacy of the automated mechanical repositioning chairs treatment for patients with benign paroxysmal positional vertigo. The Journal of Laryngology and Otology, in press.

[13] Hu, C., Zhu, K., Huang, K., Yu, B., Jiang, W., Peng, K., & Wang, F.* (2022). Using natural intervention to promote subjective well-being of essential workers during public-health crises: A study during the COVID-19 pandemic. Journal of Environmental Psychology, 79: 101745.

[14] Zhang, H., Zhu, K., Wang, J., & Lv, X.* (2022). The use of a new classification in endovascular treatment of dural arteriovenous fistulas. Neuroscience Informatics, 2(2): 100047.

[15] Zhu, K.†, Jiang, Y.†, Wang, X., Shi, Z., Yang, C.*, Liu, H.*, & Deng, K.* (2022). A new framework of customized production product certification based on the combination of domain knowledge and data inference (in Chinese). Chinese Journal of Applied Probability and Statistics, 38(4), 581–602.