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Causal reasoning is a crucial part of science and human intelligence. Inorder to discover causal relationships from data, we need structure discoverymethods. We provide a review of background theory and a survey of methods forstructure discovery. We primarily focus on modern, continuous optimizationmethods, and provide reference to further resources such as benchmark datasetsand software packages. Finally, we discuss the assumptive leap required to takeus from structure to causality.

D'ya like DAGs? A Survey on Structure Learning and Causal Discovery