Publications

Heng, S.^#✉, Shen, Y.^#, Guo, Z.^✉ (2026).
Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores.
Technical Report

Xiong, X., Guo, Z., Zhu, H., Hong, C., Smoller, J. W., Cai, T., Liu, M^✉. (2026).
Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI.
Technical Report

Koo, T. and Guo, Z. (2025).
Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts.
Technical Report

Zheng, M., Bonvini, M.^✉,and Guo, Z.^✉(2025).
Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length
Technical Report

Guo, Z., Wang, Z., Hu, Y., and Bach, F.(2025).
Statistical Analysis for Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
Technical Report

Wang, Z., Liu, M., Lei, J., Bach, F.^✉, and Guo, Z^✉.(2025).
StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
Technical Report

Gu, Y., Fang, C., Xu, Y., Guo, Z., and Fan, J.(2025).
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
Technical Report

Wang, Z.^#, Hu, Y.^#, Bühlmann, P.^✉, and Guo, Z.^✉(2024).
Causal Invariance Learning via Efficient Nonconvex Optimization
Technical Report

Rakshit, P., and Guo, Z.(2024).
Statistical Inference in High-dimensional Poisson Regression with Applications to Mediation Analysis
Technical Report

Wang, Z., Si, N., Guo, Z., and Liu, M.(2024).
Multi-source stable variable importance measure via adversarial machine learning
Technical Report

Zhan, K., Xiong, X., Guo, Z., Cai, T., and Liu, M.(2024).
Domain Adaptation Optimized for Robustness in Mixture Populations.
Technical report

*Fan, Q., Guo, Z., Mei, Z.,and Zhang, C. (2023).
Inference for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity.
Technical report

Xiong, X., Guo, Z.^✉, and Cai, T.^✉(2023).
Distributionally Robust Transfer Learning
Technical Report

Liu, Y., Liu, M., Guo, Z. and Cai, Tianxi. (2023).
Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Biobank Studies.
Technical Report

Wang, Z., Bühlmann, P., and Guo, Z. (2026).
Distributionally Robust Learning for Multisource Unsupervised Domain Adaptation.
Annals of Statistics, 20(1), 718–770.

Guo, Z., Zheng, M.,and Bühlmann, P. (2026).
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
Journal of Machine Learning Research, to appear.

*Guo, Z., Yuan W. and Zhang, C. (2026).
Decorrelated Local Linear Estimator: Inference for Non-linear Effects in High-dimensional Additive Models.
Journal of Machine Learning Research, 27(27), 1-79.[Codes]

Chang, T. H., Guo, Z., and Malinsky, D. (2026).
Post-selection inference for causal effects after causal discovery.
Biometrika, 113(1), asaf073.

Scheidegger, C., Guo, Z., and Bühlmann, P.(2026).
Inference for Heterogeneous Treatment Effects with Efficient Instruments and Machine Learning
Electronic Journal of Statistics, 20(1), 718–770.

Guo, Z., Li, X., Han, L., and Cai Tianxi. (2025).
Robust Inference for Federated Meta-Learning
Journal of the American Statistical Association, 120(551), 1695-1710. [Codes]

Scheidegger, C., Guo, Z., and Bühlmann, P. (2025).
Spectral deconfounding for high-dimensional sparse additive models
ACM/IMS Journal of Data Science, 2(1), Article 1, 52 pages.

Lin, Y., Guo, Z., Sun, B., and Lin, Z.(2025).
Testing High-Dimensional Mediation Effect with Arbitrary Exposure-Mediator Coefficients
Test, 34(3), 530-579.

Carl, D., Emmenegger, C., Bühlmann, P., and Guo, Z.(2025).
TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments
Journal of Statistical software, 114(7), 1-21. [Codes]

*Fan, Q., Guo, Z.,and Mei, Z. (2025).
A Heteroscedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates
Journal of Business & Economic Statistics, 43(2), 413-422.

Guo, Z. (2024).
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies .
Journal of the American Statistical Association, 119(547), 1968-1984. [Codes]

Kang, H., Guo, Z., Liu, Z., and Small, D.(2025).
Identification and Inference with Invalid Instruments
Annual Review of Statistics and Its Application, 12, 385–405.

Yao, M., Miller, G., Vardarajan, B., Baccarelli, A., Guo, Z.^✉,and Liu, Z.^✉ (2024).
Deciphering proteins in Alzheimer’s disease: A new Mendelian randomization method integrated with AlphaFold3 for 3D structure prediction.
Cell Genomics, 4(12), 100700.

Ma, R., Guo, Z.,Cai, T. T., and Li, H.(2024).
Statistical Inference of Genetic Relatedness using High-Dimensional Logistic Regression.
Statistica Sinica, 34 (2): 1023-1043.

Rakshit, P.,Wang, Z.,Cai, T. T., and Guo, Z. (2024).
An R Package for Statistical Inference in High-dimensional Linear and Logistic Regression Models..
R Journal, 16(3), 27-45.

*Cai, T., Guo, Z. and Xia, Y. (2023).
Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models
Test (with discussion), 32(4), 1135-1171. [Codes]

Guo, Z. (2023).
Causal Inference with Invalid Instruments: Post-selection Problems and A Solution Using Searching and Sampling.
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 85(3), 959-985. [Codes]

* Cai, T. T., Guo, Z., and Ma, R. (2023).
Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes.
Journal of the American Statistical Association, 118 (542), 1319-1332. [Codes]

Koo, T., Lee, Y., Small, D., Guo, Z.(2023).
RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables
Observational Studies, 9(4), 97-120. [Codes]

Hou, J., Guo, Z., and Cai, T. (2023).
Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction.
Journal of Machine Learning Research, 24(265), 1-58. [Codes]

Wang, X., Zhou, H., · · ·, 4CE, Avillach, P.^✉, Guo, Z.^✉, and Cai, T.^✉ (2022)
Surv-Maximin: Robust Federated Approach to Transporting Survival Risk Prediction Models.
Journal of Biomedical Informatics, 134 (2022): 104-176.

Guo, Z. , Cevid, D., and Bühlmann, P. (2022).
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding.
Annals of Statistics, 50 (3), 1320 - 1347. [Codes]

*Guo, Z. and Zhang, C. (2022).
Extreme eigenvalues of nonlinear correlation matrices with applications to additive models.
Stochastic Processes and Their Applications, 150, 1037-1058

Guo, Z., Renaux, C., Bühlmann, P., and Cai, T. T. (2021).
Group Inference in High Dimensions with Applications to Hierarchical Testing.
Electronic Journal of Statistics, 15(2), 6633-6676. [Codes]

Guo, Z., Rakshit, P., Herman, D., and Chen, J. (2021).
Inference for the Case Probability in High-dimensional Logistic Regression.
Journal of Machine Learning Research, 22(254), 1-54 [Codes]

* Cai, T, Cai, T. T. and Guo, Z. (2021).
Optimal Statistical Inference for Individualized Treatment Effects in High-dimensional Models.
Journal of the Royal Statistical Society: Series B, 2021, 83(4): 669-719. [Codes]

* Cai, T. T. and Guo, Z. (2020).
Semi-supervised Inference for Explained Variance in High-dimensional Linear Regression and Its Applications.
Journal of the Royal Statistical Society: Series B, 82(2), 391-419.

Guo, Z., Wang, W., Cai, T. T. and Li, H. (2019).
Optimal Estimation of Genetic Relatedness in High-dimensional Linear Models.
Journal of the American Statistical Association, 114(525), 358-369. [Codes]

Guo, Z., Kang, H., Cai, T. T. and Small, D. S. (2018).
Testing Endogeneity with High Dimensional Covariates.
The Journal of Econometrics, 207(1), 175-187. [Codes]

Guo, Z., Kang, H., Cai, T. T. and Small, D. S. (2018).
Confidence Intervals for Causal Effects with Invalid Instruments using Two-Stage Hard Thresholding with Voting.
Journal of the Royal Statistical Society: Series B, 80(4), 793-815. [Codes]

* Cai, T. T. and Guo, Z. (2018).
Accuracy Assessment for High-dimensional Linear Regression.
The Annals of Statistics, 46(4), 1807-1836.

Guo, Z., Small, D. S., Gansky, S. A., and Cheng, J. (2018).
Mediation Analysis for Count and Zero-Inflated Count Data without Sequential Ignorability.
Journal of the Royal Statistical Society: Series C, 67(2), 371-394. [Codes]

Cheng J., Cheng, N. F., Guo, Z., Gregorich, S., Ismail, A. I. and Gansky, S. A. (2018).
Mediation Analysis for Count and Zero-Inflated Count Data.
Statistical Methods in Medical Research, 27(9), 2756-2774. [Codes]

* Cai, T. T. and Guo, Z. (2017).
Confidence Intervals for High-Dimensional Linear Regression: Minimax Rates and Adaptivity.
The Annals of Statistics, 45(2), 615-646. [Codes]

Guo, Z. and Small, D. S. (2016).
Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models.
Journal of Machine Learning Research, 17(100):1-35, 2016. [Codes]

Guo, Z., Cheng, J.,Lorch, S. A., and Small, D. S. (2014).
Using an Instrumental Variable to Test for Unmeasured Confounding.
Statistics in Medicine, 33, 3528 - 3546.

Guo, Z., Kogan, R., Qiu, H., and Strichartz, R. S.(2014).
Boundary value problems for a family of domains in the Sierpinski gasket.
Illinois Journal of Mathematics, 58, 497 - 519.