Nonstandard Inference
Standard Wald approximations can fail after data-driven selection, for nonsmooth or optimization-defined targets, when nuisance functions are learned too slowly, or when uncertainty in an estimated design mechanism must be propagated into the final analysis. My work develops perturbation, resampling, searching, and union-based procedures that provide valid confidence statements in these nonregular settings.
Related work 6
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Heng, S.#✉, Shen, Y.#, Guo, Z.✉ (2026).
Propensity Score Propagation: A General Framework for Design-Based Inference with Unknown Propensity Scores.
Technical Report
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Chang, T. H., Guo, Z., and Malinsky, D. (2026).
Post-selection inference for causal effects after causal discovery.
Biometrika, 113(1), asaf073.
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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. [Code]
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Zheng, M., Bonvini, M.✉, and Guo, Z.✉ (2025).
Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length
Technical Report
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Guo, Z. (2024).
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies.
Journal of the American Statistical Association, 119(547), 1968-1984. [Code]
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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. [Code]