Zijian Guo
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Statistics · Causal Inference · Data Science

Zijian Guo

Qiushi Chair Professor 求是讲席教授
Center for Data Science, Zhejiang University

I develop statistical foundations for reliable and generalizable inference in modern data settings where classical assumptions often fail.

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Portrait of Zijian Guo

Profile

I am a Qiushi Chair Professor in the Center for Data Science at Zhejiang University. I received my B.S. in Mathematics from The Chinese University of Hong Kong in 2012 and my Ph.D. in Statistics from the University of Pennsylvania in 2017, advised by T. Tony Cai.

From 2017 to 2025, I served on the faculty of the Department of Statistics at Rutgers University, advancing from Assistant Professor to tenured Associate Professor, before returning to China to join Zhejiang University.

Research

My research develops statistical foundations for reliable and generalizable inference in modern data settings where classical assumptions often fail.

I study how to make statistical conclusions stable and trustworthy under hidden confounding, invalid instruments, multi-source heterogeneity, distribution shift, and nonregularity. Across these areas, I use tools from optimization and machine learning to define robust statistical targets, design computable procedures, and develop valid uncertainty quantification when standard Wald-style approximations become unreliable. I am also interested in applications to genetics, proteomics, and multi-center health data.

Reliable causal inference

Identification and causal-effect inference under invalid instruments, endogeneity, and hidden confounding.

Multi-source learning

Transfer, federation, and stable learning across heterogeneous populations and environments.

Nonstandard inference

Valid confidence statements after selection, slow nuisance estimation, and other nonregular behavior.

High-dimensional uncertainty quantification

Confidence intervals and testing when parameters rival or exceed observations.

Genetics and health data

Methods driven by genomics, proteomics, biomarkers, and multi-center clinical data.

Distributionally Robust Learning and Optimization

Statistical formulations and algorithms for distributional robustness and structured nonconvex problems.

Updates

View all publications
  1. 2017

    Confidence Intervals for High-Dimensional Linear Regression: Minimax Rates and Adaptivity

    * T. T. Cai and Z. Guo · Annals of Statistics, 45(2), 615–646 · Code

  2. 2018

    Confidence Intervals for Causal Effects with Invalid Instruments Using Two-Stage Hard Thresholding with Voting

    Z. Guo, H. Kang, T. T. Cai and D. S. Small · Journal of the Royal Statistical Society: Series B, 80(4), 793–815 · Code

  3. 2022

    Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding

    Z. Guo#✉, D. Ćevid# and P. Bühlmann · Annals of Statistics, 50(3), 1320–1347 · Code

  4. 2023

    Causal Inference with Invalid Instruments: Post-selection Problems and a Solution Using Searching and Sampling

    Z. Guo · Journal of the Royal Statistical Society: Series B, 85(3), 959–985 · Code

  5. 2024

    Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies

    Z. Guo · Journal of the American Statistical Association, 119(547), 1968–1984 · Code

  6. 2024

    Deciphering Proteins in Alzheimer’s Disease: A New Mendelian Randomization Method Integrated with AlphaFold3 for 3D Structure Prediction

    M. Yao, G. Miller, B. Vardarajan, A. Baccarelli, Z. Guo✉, and Z. Liu✉ · Cell Genomics, 4(12), 100700

Selected preprints

  1. 2026

    Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores

    S. Heng#✉, Y. Shen#, and Z. Guo✉ · Technical Report

  2. 2025

    Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts

    T. Koo and Z. Guo · Technical Report

  3. 2025

    Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length

    M. Zheng, M. Bonvini✉, and Z. Guo✉ · Technical Report

  4. 2025

    Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss

    Z. Guo, Z. Wang, Y. Hu, and F. Bach · Technical Report

  5. 2024

    Causal Invariance Learning via Efficient Nonconvex Optimization

    Z. Wang#, Y. Hu#, P. Bühlmann✉, and Z. Guo✉ · Technical Report

Authorship notation Name supervised student # equal contribution * alphabetical ordering ✉ corresponding author

Honors & awards

  • 2023

    ICSA Outstanding Young Researcher Award

  • 2023

    Honorary Mention, Bernoulli Society New Researcher Award

  • 2019

    ICSA New Researcher Award, ICSA International Conference

  • 2017

    IMS Travel Award, JSM

  • 2017

    President Gutmann Leadership Award, University of Pennsylvania

  • 2016

    J. Parker Bursk Prize

  • 2013

    Statistics in Epidemiology Young Investigator Award, JSM

Academic service

I serve as an Associate Editor for the following journals.

  • Associate Editor · 2023–present

    JASA

    Journal of the American Statistical Association
  • Associate Editor

    TEST

    An Official Journal of the Spanish Society of Statistics and Operations Research
  • Associate Editor

    EJS

    Electronic Journal of Statistics

Conference service: Chair, Local Organizing Committee, 2026 International Conference on Frontiers of Data Science.

Opportunities

I welcome students and research interns interested in causal inference, multi-source learning, distributionally robust learning and optimization, and uncertainty quantification.

PhD applicants

Send your CV and transcripts by email after reviewing the group’s research areas.

Research interns

Strong preparation in probability, statistics, and scientific computing is especially welcome.

Get in touch