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Elements of Causal Inference is an important contribution to the growing literature on causal analysis. This lucid monograph elegantly weaves together statistics, machine learning, and causality to provide a holistic picture of how we and machines can use data to understand the world.

―David Blei, Professor of Computer Science and Statistics, Columbia University

Causal inference is a well-established field in statistics, but it is still relatively underdeveloped within machine learning. This is partly due to the lack of good learning resources before Elements of Causal Inference came along. This book is high-quality work that breaks through, firmly establishing a connection between causal inference and general machine learning.

―Ricardo Silva, Senior Lecturer, University College London; Turing Fellow, Alan Turing Institute

Preface

Notation and Terminology

1 Statistical and Causal Models
2 Assumptions for Causal Inference
3 Cause-Effect Models
4 Learning Cause-Effect Models
5 Connections to Machine Learning,Ⅰ
6 Multivariate Causal Models
7 Learning Multivariate Causal Models
8 Connections to Machine Learning,Ⅱ
9 Hidden Variables
10 Time Series

Appendices
Appendix A Some Probability and Statistis
Appendix B Causal Orderings and Adjacency Matrices
Appendix C Proofs
Bibliography
Index

A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.

After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.

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. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.