The Causal Inference Book: DAGS and more

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:

In SAS, Stata, MS Excel, and CSV formats
Codebook

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!

Our revised #CausalInferenceBook is now freely available.

The book is organized in 3 parts of increasing difficulty: From counterfactuals and causal diagrams to treatment-confounder feedback and g-methods.

Thanks to everyone who sent us comments/typos.https://t.co/bRPFYazK2D
— Miguel Hernán (@_MiguelHernan) January 1, 2019

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.

Last week my wife was out of town, research was slow, and summer self-study aspirations were high, so I sat down and organized:

ALL THE 50+ DAGS from @_MiguelHernan and Robins Causal Inference book (P1):https://t.co/vKcdclTQbe

Some notes on key conceptshttps://t.co/IcLZCLlrZW pic.twitter.com/UZWSauvQxT
— Sam Finlayson (@IAmSamFin) June 20, 2019