arXiv (Cornell University) · 2024 · 36 citations · 0 references
ComputingComputer ScienceData ScienceArtificial IntelligenceCausal InferenceMachine LearningAutomated ReasoningBayesian InferenceCausal Effect EstimationCausal Graphical ModelsDirected Acyclic GraphsStructural Equation ModelingCausal Effect IdentificationCausal Inference From DataCausal Machine LearningCausal ReasoningDirected Acyclic Graph LearningDouble Machine LearningGraphical Causal ModelsMachine Learning Analytics
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.