Harvard (bio)statisticians Miguel Hernan and Jamie Robins just released their new book, online and accessible for free!
The Causal Inference book provides a cohesive presentation of causal inference, its concepts and its methods. The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data. Here’s the official Harvard page for the book release.
Some of the book’s (NHEFS) data is accesible too:
As is the associated computer code for the analyses, in multiple languages:
- R by Joy Shi and Sean McGrath. Rendered version by Tom Palmer.
- Python by James Fiedler
- SAS by Roger Logan
- Stata by Eleanor Murray and Roger Logan
This is definitely an interesting read for epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, data scientists, computer scientists, and any other person with a love for proper data analysis!
Sam Finalyson visualized some of the Directed Acyclic Graphs (DAG) covered in the book, and these also look quite nice. The visuals and other notes and glossary items here.
Cover image via blytheadamson.com