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 is 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.
Thus, this workshop aims at bringing together researchers and students from Artificial Intelligence, Machine Learning, Statistics and Cognitive Science who work on causal discovery, the construction of causal models and their evaluation, as well in practical applications. We welcome original research as well as work in progress.
The third edition of CaDis will take place at IIIA in Xalapa, Veracruz, México, which will be held in a hybrid format, in person and virtual.
CaDis 