01528nas a2200157   4500008004100000245006400041210006400105490000700169520099300176100002401169700002001193700002401213700002001237700001601257856009701273       2022                        eng d00aMethods and tools for causal discovery and causal inference0 aMethods and tools for causal discovery and causal inference0 v123 aCausality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples.1 aNogueira, Ana, Rita1 aPugnana, Andrea1 aRuggieri, Salvatore1 aPedreschi, Dino1 aGama, João  uhttps://kdd.isti.cnr.it/publications/methods-and-tools-causal-discovery-and-causal-inference