We are writing a book on causal reasoning with an explicit focus on computing systems. We will be posting book chapters here as we complete them.
Update: The first chapter is out! Do share your feedback in the comments.
Causal Reasoning: Fundamentals and Machine Learning Applications
Other great books
For a casual introduction to causality:
- Pearl. “The Book of Why: The New Science of Cause and Effect” [Link]
For a general introduction that covers both potential outcome and graphical model frameworks:
- Morgan, Winship. “Counterfactuals and Causal Inference: Methods and Principles for Social Research” [Link]
For a technical introduction, accessible to most:
- Pearl, Glymour, Jewell. “Causal Inference in Statistics: A Primer” [Link]
For an econometric view, with a focus on local identification:
- Angrist, Pischke. “Mastering Metrics: The Path from Cause to Effect” [Link]
For statistical estimation and design of analysis:
- Rosenbaum, “Design of Observational Studies”[Link]
For connections to machine learning:
- Peters, Janzing, Schoelkopf. “Elements of Causal Inference: Foundations and Learning Algorithms”[Link]