Causal Inference：What If 电子书、代码、

Causal Inference Book

来源：https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

Miguel Hernan & Jamie Robinshave written a book that 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. We 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.

Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

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

• The Causal Inference book (updated 21 February 2020)
• 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 by James Fiedler
  • Parametric g-formula software in R and SAS

Warning: At this stage, we may still revise and correct errors without documenting the changes. Please make sure you use the most updated version of the book posted here.