Bayesian Statistics and Causal Inference
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: closed (25 November 2023) | Viewed by 1653
Special Issue Editors
Interests: statistical analysis; Bayesian inference; high-dimensional data analysis; probabilistic graphical models
Special Issue Information
Dear Colleagues,
In recent decades, causal inference and Bayesian statistics have experienced remarkable developments due to the rise in the interest of scholars across many fields. Causal inference aims to estimate the causal effects of a treatment or an exposure on a response of interest. This task is of paramount importance in many contexts, including, for example, medicine, economics and public health. Still, drawing causal conclusions from data requires assumptions and methods that differ from those used in traditional associational studies. Bayesian statistics provides a way to combine researchers’ prior information with that coming from data. In recent years, some attempts have been made to integrate the two approaches to exploit their strengths. This Special Issue is open to methodological and applied works which can provide insightful contributions to the topic and show the advantages of combining the two ‘worlds’. Examples of possible subjects include, but are not limited to, high-dimensional data, graphical models, missing data, machine learning, matching methods, nonparametric estimation and computational aspects. Contributions from different fields are welcome.
Dr. Antonino Abbruzzo
Dr. Chiara Di Maria
Guest Editors
Manuscript Submission Information
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Keywords
- causal inference
- Bayesian statistics
- treatment effects
- missing data
- nonparametric models
- machine learning
- high-dimensional data
- graphical models
