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

This book provides a comprehensive introduction to causal inference, an increasingly important area of data science and machine learning.

With a focus on practical applications, the authors offer a self-contained and concise guide to using causal models in real-world scenarios.

The book 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 authors also discuss analyzing statistical asymmetries between cause and effect, providing valuable insights into the problem of bivariate causal learning.

The book is accessible to readers with a background in machine learning or statistics and can be used in graduate courses or as a reference for researchers.

With code snippets, exercises, and an appendix summarizing technical concepts, this book is an essential resource for anyone looking to master the art of causal inference.