
Ruoqi Yu
· Assistant ProfessorUniversity of Illinois Urbana-Champaign · Statistics
Active 1984–2026
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About
Ruoqi Yu is an Assistant Professor in the Department of Statistics at the University of Illinois. Her research primarily focuses on causal inference, particularly in observational studies and factorial experiments. She has contributed significantly to the development of methods for covariate balancing, matching techniques, and treatment effect estimation. Her work includes advancing balancing weights for causal inference in observational factorial studies and exploring the effectiveness of fine balance for covariate balancing. Yu has also investigated near-far matching methods for instrumental variables study designs with large observational datasets, addressing important applications such as maternal complications after cesarean delivery. Her research outputs demonstrate a strong emphasis on methodological innovations in statistics to improve the accuracy and reliability of causal conclusions drawn from complex observational data.
Research topics
- Chemistry
- Physics
- Nanotechnology
- Materials science
- Atomic physics
- Chemical physics
- Geometry
- Metallurgy
- Astrobiology
- Optoelectronics
Selected publications
The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
ArXiv.org · 2025-09-14
preprintOpen accessSenior authorRecursive decision trees are widely used to estimate heterogeneous causal treatment effects in experimental and observational studies. These methods are typically implemented using CART-type recursive partitioning and are often viewed as adaptive procedures capable of discovering treatment effect heterogeneity in high-dimensional settings. We study causal tree estimators based on adaptive recursive partitioning and establish lower bounds on their estimation accuracy. Under basic conditions, we s…
Boundary Discontinuity Designs: Theory and Practice
arXiv (Cornell University) · 2025-11-09
preprintOpen accessSenior authorThe boundary discontinuity (BD) design is a non-experimental method for identifying causal effects that exploits a thresholding rule based on a bivariate score and a boundary curve. This widely used method generalizes the univariate regression discontinuity design but introduces unique challenges arising from its multidimensional nature. We synthesize over 80 empirical papers that use the BD design, tracing the method's application from its formative stages to its implementation in modern resear…
Distributed Coherent Beamforming at 60 GHz Enabled by Optically-Established Coherence
ArXiv.org · 2025-09-17
preprintOpen accessWe implement and experimentally demonstrate a 60 GHz distributed system leveraging an optical time synchronization system that provides precise time and frequency alignment between independent elements of the distributed mesh. Utilizing such accurate coherence, we perform receive beamforming with interference rejection and transmit nulling. In these configurations, the system achieves a coherent gain over an incoherent network of N nodes, significantly improving the relevant signal power ratios.…
eLife · 2025-12-01
articleOpen access
Frequent coauthors
- 159 shared
Jing Zhu
- 98 shared
Wandong Xing
Fuzhou University
- 56 shared
Zhiying Cheng
State Key Laboratory of New Ceramics and Fine Processing
- 41 shared
Haozhi Sha
University of California, Los Angeles
- 34 shared
Yadong Li
Anhui Normal University
- 33 shared
Yang Zhang
- 32 shared
Shuai Dong
- 32 shared
Fanyan Meng
Education
- 2002
Ph.D., Shenyang National Laboratory for Materials Science
Institute of Metal Research Chinese Academy of Sciences
- 1999
Master, Laboratory of Atomic Imaging of Solids
Institute of Metal Research Chinese Academy of Sciences
- 1996
Bachelor, Department of Materials Science and Engineering
Zhejiang University
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