Applied Causal Inference Powered by ML and AI
arXiv:2403.02467v159 citationsh-index: 80
AI Analysis
This is an introductory book aimed at researchers and practitioners, presenting existing methods without new results.
The paper introduces the integration of machine learning with causal inference, covering classical structural equation models and modern methods like directed acyclical graphs and Double/Debiased Machine Learning for inference.
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.