Advancements in Causal Discovery Algorithms: Theory and Applications
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 44
Special Issue Editor
Special Issue Information
Dear Colleagues,
Causal discovery is a crucial field that aims to uncover causal relationships among variables from passively observational data, and it is vital for understanding underlying mechanisms, predicting outcomes, making informed decisions, and counterfactual reasoning in complex systems. The process of causal discovery involves sophisticated statistical and computational techniques. Over the years, notable progress has been achieved in causal discovery, even in complex scenarios featuring distribution shifts, hidden confounders, selection bias, cycles, measurement error, etc. Furthermore, the learned causal knowledge further holds promise for various fields, spanning from AI to various scientific disciplines. For instance, in healthcare it helps identify the causes of diseases and the effects of treatments, improving patient outcomes. In marketing, causal discovery can optimize strategies by revealing the true drivers of consumer behavior. Moreover, it is instrumental in scientific research, where uncovering causal relationships is essential for develo** new theories and technologies.
The aim of this Special Issue is to collect recent developments on causal discovery algorithms and their applications to real-case studies. The topics include, but are not limited to, the following:
- Causal discovery in complex environments, e.g., in the presence of distribution shifts, latent confounders, selection bias, cycles, measurement error, small samples, or missing data;
- Efficient causal discovery in large-scale datasets;
- Real-world applications of causal discovery, e.g., in neuroscience, finance, climate, and biology;
- Assessment of causal discovery methods and benchmark datasets;
- Causal perspectives on the problem of generalizability, transportability, transfer learning, and life-long learning;
- Causally enriched reinforcement learning and active learning;
- Disentanglement, representation learning, and develo** safe AI from a causal perspective;
- Causality in foundation models.
Dr. Biwei Huang
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
- causal discovery
- causality for AI/ML
- causality for scientific discovery
- foundation models
