Helpful resources

Nice! You want to learn about the foundations of causal inference.

These resources are a good starting point:

• 📚 Causality: Models, Reasoning, and Inference by Judea Pearl. This comprehensive introduction to the topic, covering all basic definitions, is a solid starting point.
• 📚 Causation, Prediction, and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. This book covers the PC and the FCI algorithm in their original form, together with several real-world applications. This helps to gain intuition on how one can apply these algorithms to real-world data; however, some use cases are really only helpful in demonstrating how the algorithms work. Intuitions for a critical analysis of your potential use cases are discussed here.
• 🎓 Graphical Characterization of Adjustment Sets by Emilija Perković. The introduction of this PhD thesis from ETH Zurich provides a nice summary of the causal inference pipeline that consists of causal discovery – potentially combined or even replaced with expert knowledge – and causal effect estimation. It then discusses the identification of adjustment sets for causal effect estimation.
• 👩🏽‍💻 List of causality projects on GitHub. Are you looking for a method that Causy does not cover (yet)? You might find it in this (incomplete) list of causality projects on GitHub. For non-time-series data, start with causal learn; for time-series data, take a look at tigramite.