  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.
