Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)

This book provides a comprehensive introduction to causal inference, a topic increasingly important in data science and machine learning. It covers how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas can be used for classical machine learning problems. It is accessible to readers with a background in machine learning or statistics and includes code snippets, exercises, and an appendix with important technical concepts.