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: closed (31 March 2025) | Viewed by 1890
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 developing 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 developing safe AI from a causal perspective;
- Causality in foundation models.
Dr. Biwei Huang
Guest Editor
Manuscript Submission Information
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Keywords
- causal discovery
- causality for AI/ML
- causality for scientific discovery
- foundation models
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