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]