2024: Workshop on Causal Discovery CaDis 2024

This volume contains the proceedings of the 2nd Workshop on Causal Discovery (CaDis 2024). The workshop was held at the Facultad de Ingeniería of the Universidad de la República in Montevideo, Uruguay as part of IBERAMIA 20224. This edition was made possible through the collaboration of the National Institute of Astrophysics, Optics, and Electronics of Mexico (INAOE), the National Center for Artificial Intelligence (CENIA) of Chile, and the Iberamia organizing committee. We acknowledge ths support of the Artificial Intelligence Journal, and the Mexican Academy of Computing (AMexComp). Causal models have many advantages, including the ability to reason about the effects of interventions, as well as the results of different scenarios or counterfactuals. The traditional approach for building causal models is by conducting experiments, however these are often infeasible, unethical or too expensive. Recently there has been a lot of interest in the scientific community to learn causal models from observational data, but this is a great challenge, as just from observations is not possible, in general, to define a unique causal model. The objective of this workshop was to present recent advances in causal discovery, including different approaches that consider observational and/or interventional data, and also building models with the help of human experts. It is also of interest the combination of causal discovery with other areas of machine learning, such as reinforcement learning and deep learning; as well as real-world applications.