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

This book offers a comprehensive introduction to causal inference, providing readers with a self-contained and concise understanding of how to learn from data.

With a focus on practical applications in data science and machine learning, the text covers topics such as computing intervention distributions, inferring causal models from observational and interventional data, and exploiting causal ideas for classical machine learning problems.

The book is accessible to readers with a background in machine learning or statistics and includes code snippets, exercises, and an appendix summarizing technical concepts.

Whether you're a student looking for a graduate course reference or a researcher seeking a comprehensive guide, this book is the perfect resource for anyone interested in causal inference.