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Uncertainty Quantification and Causality

[Kernel methods, Statistical inference, Causal inference]

(Note:     students under my supervision;  * corresponding author.)

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Kernel methods​

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• Kernel knockoffs selection for nonparametric additive models. [journal][reprint][preprint][code]

Xiaowu Dai*, Xiang Lyu, and Lexin Li.

Journal of the American Statistical Association: Theory and Methods (JASA), 2023. [lead article].

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• Nonparametric estimation via partial derivatives. [journal​][preprint][UCLA StatsDS News​][UCLA Physical Sciences News​]

Xiaowu Dai*. 

Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2024. [lead article]

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• Another look at statistical calibration: A non-asymptotic theory and prediction-oriented optimality. [pdf]

Xiaowu Dai and Peter Chien. 

Preprint, 2023.

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• Multi-layer kernel machines: Fast and optimal nonparametric regression with uncertainty quantification. [pdf][code][PyPI]

Xiaowu Dai* and Huiying Zhong.

Preprint, 2024.

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• Variance reduction via resampling and experience replay. [pdf][code]

Jiale Han, Xiaowu Dai*, and Yuhua Zhu.

Preprint, 2025.

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Statistical inference

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• Discussion: "Estimating means of bounded random variables by betting" by Waudby-Smith and Ramdas. [journal][reprint​][preprint][code]

Jiayi Li, Yuantong Li, and Xiaowu Dai*.

Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2023.

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• Conformal online auction design. [pdf][code][older version, code]

Jiale Han and Xiaowu Dai*.   

Under R&R at Journal of the American Statistical Association (JASA), 2025.

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• Selection and estimation optimality in high dimensions with the TWIN penalty. [pdf]

Xiaowu Dai and Jared Huling. 

Preprint, 2023.

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Causal inference

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• Orthogonalized kernel debiased machine learning for multimodal data analysis. [journal][reprint][preprint]

Xiaowu Dai and Lexin Li.

Journal of the American Statistical Association: Theory and Methods (JASA), 2022. [lead article]