3 accepted papers
Causal discovery is the task of learning causal models, encoding causal relationships, from a source of information, such as a dataset containing observational data. While many algorithms have been developed to discover causal models under varied sets of assumptions, the case in which the dataset is
Causal discovery is the task of learning a causal model from a source of information. Traditionally, the community has focused on algorithms that infer causal models from observational and/or interventional data, while alternative approaches have been only marginally explored. The proposed work aims
In this work, we prove that agents capable of adapting to distribution shifts must have learned the causal model of their environment even in the presence of mediation. This term describes situations where an agent's actions affect its environment, a dynamic common to most real-world settings. For e…