What If – Causal Inference (free)

Miguel Hernán made a revision of book “Causal Inference: What If” is available for download.

The book provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles.
The authors expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists…

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.

You can download it for free from this page.

The components of the book can be accessed by clicking on the links below:

•  Causal Inference: What If (preprint, 2020; revised 2023)
• NHEFS data
  • In SAS, Stata, MS Excel, and CSV formats
  • Codebook
• Computer code
  • SAS by Roger Logan
  • Stata by Eleanor Murray and Roger Logan
  • R by Joy Shi and Sean McGrath. Rendered version by Tom Palmer.
  • Python and Julia by James Fiedler
  • Parametric g-formula software in R and SAS