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arxiv:2403.02467
Applied Causal Inference Powered by ML and AI
Published on Mar 4, 2024
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Abstract
The intersection of machine learning and causal inference is explored through structural equation models, directed acyclic graphs, structural causal models, and double/debiased machine learning techniques.
AI-generated summary
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.
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