DAGitty's functions are described in the PDF manual. However, the manual provides only very little introduction to DAGs themselves. My recommended resource for learning about them is the book "Causal Inference in Statistics: A Primer" by Pearl, Glymour and Jewell. The Primer also contains exercises, many of which can be solved using DAGitty and the DAGitty R package. See the R vignette for the Primer.

If you are just getting started with DAGitty and the manual seems like a little much, check out the DAGitty primer/cheat sheet. It will get you started in using DAGitty to draw and evaluate causal diagrams.

Below you can find some other resources for learning about DAGs and DAGitty.

This is a growing list of interactive tutorials about DAGs that are built in DAGitty itself.

- Parents, children, ancestors, descendants ... If you are confused by all the graph terminology, here's our little game to learn about it!
- What is a confounder? A mediator? A proxy confounder? These are all concepts that can only be defined by invoking causal language. Learn in our tutorial on covariate roles how to spot such variables in DAGs.

- The article d-Separation Without Tears, adapted from Judea Pearl's textbook "Causality", explains the key concept of d-separation in simpler words.

- "Table II Fallacy" is a nice idiom introduced by Westreich and Greenland. It refers to the problems with interpreting the coefficients in a multiple regression model causally, including the widespread belief that coefficients in such models are "mutually adjusted". Learn here how to properly interpret coefficients in multiple regression models!

- The Simpson Machine illustrates which causal structures can lead to Simpson's paradox, and that valid covariate adjustment sets cannot be found without causal considerations.

- The Single-Door Criterion can be used to identify structural parameters (direct effects) in structural equation models, even when the model as a whole is not identifiable. This short tutorial explains what the single-door criterion can, and cannot, do.

**Make your own example!** If you know a little HTML
and JavaScript, you can make your own interactive example like
above. Read here how!

Some nice people have gone through the effort to make tutorial videos about how to use dagitty. They are mainly based on 2.x versions, but still worth checking out:

Tutorial by
Nick Huntington-Klein,
CSU Fullerton

Tutorial by
Nisha C. Gottfredson,
UNC

Tutorial (video only) by
Jari Haukka,
University of Helsinki

Tutorial by
Scott Venners, SFU

- Thinking Clearly About Correlations and Causation by Julia Rohrer is an excellent introduction, written from a Psychology perspective but useful for anyone.
- Graphical Causal Models by Felix Elwert is another nicely written introduction to DAGs, aimed at social scientists.
- Causal Inference and Data-Fusion in Econometrics is, as the title suggests, an introduction to the topic that's aimed at Econometricians, but I would recommend it for a broader audience as it covers many more recent developments in the field as well.
- "Reducing bias through Directed Acyclic Graphs" by Ian Shrier and Robert W. Platt is a nicely written piece on the specific issue of covariate adjustment.
- Epidemiologists may also like Causal Diagrams for Epidemiologic Research by Greenland, Pearl, and Robins.
- And then there's Judea Pearl's textbook "Causality", which is comprehensive and thought-provoking, but not extremely accessible.