Special Issue on High-Dimensional Inference and Causal Inference

Aims and Scope

Statistical Learning and Data Science (SLADS) is a newly launched journal sponsored by the Chinese Academy of Sciences, dedicated to publishing high-quality research across statistics, machine learning, artificial intelligence, and data science. SLADS emphasizes both rapid publication and rigorous peer review, using the OpenReview system to ensure transparency and quality. The editorial goal is to reach an Accept/Reject decision within 3.5 months of submission while maintaining high scholarly standards.

This special issue aims to be a collection of frontier research on theory, methodology and application of high-dimensional data inference and causal inference.

Submission Deadline: December 31, 2026

• Early submissions will be reviewed and published online ahead of the final issue.

Topics of Interest

Valid Inference for high-dimensional models

• Confidence intervals in p≫n settings
• Hypothesis testing in p≫n settings
• Uniform inference under model misspecification
• Adaptive and nonconvex regularization
• Fundamental limits and minimax theory in high dimensions

Graphical Models in High Dimensions

• Sparse precision matrix estimation
• High-dimensional time-varying graphical models
• Non-Gaussian graphical models

High-Dimensional Causal Inference

• Sparse structural equation models
• High-dimensional IV and treatment effect models
• Causal discovery under sparsity

Guest Editors

• Runze Li (Pennsylvania State University)
• Chengchun Shi (London School of Economics)
• Hui Zou (University of Minnesota)
• Yang Ning (Cornell University)
• Changliang Zou (Nankai University)

Submission Information

Manuscripts should be submitted through the SLADS website at http://slads.scichina.com.

Contact: Ruiyan Zhang, zhangry@scichina.com