Causal Inference Books

This is my first, blog. More to come soon.

I think it is interesting all the Books about Causality and Causal Inference that I have checked out so far. I will try to keep this list updated.

Don’t expect to find here summaries or reviews of the books, just a list of the books I have read or I am reading.

Pearl’s series

Judea Pearl is a pioneer in the field of causal inference and has written several influential books on the subject. His work has laid the foundation for much of the modern understanding of causality in statistics and machine learning. As far as I know, he introduced the do-calculus, a incredibly useful tool for analyzing identifiability in causal inference problems.

1. Causality: Models, Reasoning, and Inference (2000) by Judea Pearl.
   • This book introduces the concept of causal inference and provides a comprehensive framework for understanding causality.
   • It covers graphical models, do-calculus, and the relationship between causation and correlation.
2. Causal Inference in Statistics: A Primer (2016) by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell.
   • This book serves as an introduction to causal inference for statisticians and researchers.
   • It covers the basics of causal inference, including potential outcomes, counterfactuals, and causal graphs.
3. The Book of Why: The New Science of Cause and Effect (2018) by Judea Pearl and Dana Mackenzie.
   • This book is a more accessible introduction to causal inference for a general audience.
   • It discusses the importance of causality in various fields, including science, medicine, and social sciences.
   • It also emphasizes the role of causal diagrams in understanding complex systems.
   • Don’t expect a deep dive into the technical aspects of causal inference, but rather a broad overview of the concepts and their implications.

Online books

1. Causal inference for the Brave and the True (2022) by Matheus Facure.
   • This book is a comprehensive introduction to causal inference, covering both the theoretical foundations and practical applications.
   • It includes numerous examples and exercises to help readers understand the concepts.
   • The book is available for free online, making it accessible to a wide audience.
   • Link to the book
2. The effect: An introduction to causal inference (2020) by Nick Huntington.
   • This book provides a practical introduction to causal inference, focusing on the potential outcomes framework and the use of R for causal analysis.
   • It covers topics such as randomized experiments, observational studies, and causal graphs.
   • The book is available for free online, making it accessible to a wide audience.
   • Link to the book
3. Integrated Inferences: Causal Models for Qualitative and Mixed-Method Research (2023) by Macartan Humphreys and Alan M. Jacobs
   • Link to the book
4. Applied Causal Inference (2023) by Uday Kamath, Kenneth Graham, Mitchell Naylor
   • Link to the book
5. Causal Inference: The mixtape (2021) by Scott Cunningham.
   • This book is a comprehensive introduction to causal inference, covering both the theoretical foundations and practical applications.
   • It includes numerous examples and exercises to help readers understand the concepts.
   • Link to the book
   • Also available printed in Amazon.
6. Causal Factor Investing (2023) by Marcos López de Prado.
   • This book provides a comprehensive introduction to causal factor investing, covering both the theoretical foundations and practical applications.
   • It includes numerous examples and exercises to help readers understand the concepts.
   • Link to the book
   • Also available printed.

Rubin Potential outcomes framework

1. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction (2010) by Guido W. Imbens and Donald B. Rubin.
   • This book provides a comprehensive introduction to the potential outcomes framework for causal inference.
   • It covers topics such as randomized experiments, observational studies, and matching methods.
   • The book is widely used in statistics and social sciences courses.

Open access books

As far as I know, these books are open access and available for free online. I include the links to the books in the list.

1. Applied Causal Inference Powered by Machine Learning and Artificial Intelligence (2024) by Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spingler and Vasilis Syrgkanis.
   • Link to the book
2. Introduction to Causal Inference (2020) by Brady Neal
   • Link to the book
   • This book is one of my favorites recommedations for beginners, since it provides a comprehensive introduction and even more important, is accompanied by a set of fantastic videos.
   • Note that the book is not finished, but the videos are complete.
3. Causal inference: What If (2020) by Miguel Hernán and James Robins
   • Link to the book
   • One of the most celebrated references in Causal Inference, very complete.
   • Confoundinf, Selection bias, Propensity Score methods, G-formula, Survival Analysis, Time-varying confounding.
4. Probability and Causality: Conditional and Average Total Effects (2024 - in preparation) by Rolf Steyer.
   • If you come from mathematics and probability background, this book is for you.
   • Beyond Pearl do-calculus and Rubin potential outcomes framework, this book provides a mathematical approach to causality.
   • Link to their work
5. Elements of Causal Inference: Foundations and Learning Algorithms (2020) by Jonas Peters, Dominik Janzing, and Bernhard Schölkopf.
   • Probably the most cited book in my work with Causality (2009).
   • Theory about hidden confounding, Structural causal models and connections with machine learning are discussed.
   • Link to the book
6. Causation, Prediction and Search (2000) by Peter Spirtes, Clark Glymour, and Richard Scheines.
   • Probably the most celebrated book about causal discovery.
   • It covers topics such as causal discovery, graphical models, and the relationship between causation and correlation.
   • Link to the book
7. Causal Inference: A Statistical Learning Approach (2024 - draft) by Stefan Wager
   • Link to the book

Causal inference in Python

Books quite recent, that you can find in Amazon or other online stores.

1. Causal Inference and Discovery in Python (2022) by Aleksander Molak
   • Introduction to DoWhy, EconML and CI in torch.
   • Available at Amazon here.
   • Code available in github
2. Causal Inference in Python (2023) by Matheus Facure
   • Link to the book
   • Code also available in github
3. Causal Inference for Data Science (2024) by Alex Ruiz de Villa
   • Link to the book

Other references

1. Structural Equations with Latent Variables (1989) by Kenneth A. Bollen.
   • This book provides a comprehensive introduction to structural equation modeling (SEM) and latent variable analysis.
   • It covers topics such as model specification, estimation, and evaluation.
   • The book is widely used in social sciences and psychology courses.