D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
Matthew J. Vowels, Necati Cihan Camgöz, Richard Bowden
Abstract
Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery. We primarily focus on modern, continuous optimization methods, and provide reference to further resources such as benchmark datasets and software packages. Finally, we discuss the assumptive leap required to take us from structure to causality.
Topics & Concepts
Computer scienceCausal structureData scienceCausality (physics)Benchmark (surveying)Focus (optics)Causal inferenceCausal modelKnowledge extractionArtificial intelligenceEconometricsGeographyQuantum mechanicsEconomicsPathologyMedicinePhysicsGeodesyOpticsBayesian Modeling and Causal InferenceComputational Drug Discovery MethodsBioinformatics and Genomic Networks