Applied Causal Inference Powered by ML and AI

• URL: http://arxiv.org/abs/2403.02467v1
• Date: Mon, 4 Mar 2024 20:28:28 GMT
• Title: Applied Causal Inference Powered by ML and AI
• Authors: Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
• Abstract summary: The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs) It covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.
• Score: 54.88868165814996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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