Causal Relationship
Causal relationship research aims to understand and model cause-and-effect relationships within complex systems, moving beyond simple correlations. Current research focuses on developing algorithms and models, such as Bayesian networks, that can effectively learn causal structures from observational and interventional data, often incorporating expert knowledge or handling high-dimensional and incomplete datasets. These advancements are crucial for improving decision-making in various fields, including healthcare, finance, and robotics, by enabling more accurate predictions and interventions based on a deeper understanding of underlying causal mechanisms. The development of robust benchmarking frameworks further enhances the reliability and reproducibility of causal discovery methods.
Papers
ESNLIR: A Spanish Multi-Genre Dataset with Causal Relationships
Johan R. Portela, Nicolás Perez, Rubén ManriqueuniandesExMAG: Learning of Maximally Ancestral Graphs
Petr Ryšavý, Pavel Rytíř, Xiaoyu He, Georgios Korpas, Jakub MarečekCzech Technical University in Prague●HSBC Quantum Technologies Group●Archimedes Research Unit on AI