Advances in Statistical AI and Casual Inference
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 490
Special Issue Editors
Interests: high-dimensional statistics; non-asymptotic theory; functional data analysis; robust statistical learning; the mathematics of deep learning; concentration inequalities; subsampling
Special Issue Information
Dear Colleagues,
This Special Issue focuses on recent advancements in statistical models and machine learning methods at the intersection of artificial intelligence (AI) and causal inference, with applications in genomics, bioinformatics, and precision medicine. Although AI has achieved remarkable success, there remain challenges in developing statistical theory and methodology for AI. This issue particularly highlights theoretical advancements involving deep neural networks and causal inference, particularly with regard to non-asymptotic theory and small sample learning. The theoretical analysis of deep neural networks can be divided into three components: approximation, optimization, and generalization. Causal inference includes frameworks such as the Rubin causal model, mediation analysis, causal graphs, observational studies, and instrumental variables to enable our understanding of causality and reasoning. We highlight how machine learning and deep learning can be effectively integrated with causal inference, enabling researchers to address potential biases in estimating causal effects and heterogeneous causal effects. We also encourage researchers to incorporate novel insights into their empirical research and experimental design.
The sub-topics to be covered within the issue are as follows:
- deep neural networks
- finite sample theory
- non-asymptotic statistics
- precision medicine
- treatment effect estimation
- uncertainty quantification
- reinforcement learning
- adaptive experiments and bandit algorithms
- experimental design
- high-dimensional statistics
- multiple testing
Dr. Huiming Zhang
Dr. Hengzheng Huang
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- deep neural networks
- causal inference
- precision medicine
- non-asymptotic theory
- treatment effect
- statistical machine learning
