I made the first version of this back in 2010, when Pearl's work on causal inference started impacting Epidemiology. A friend was an Epidemiologist and she told me about an MS-DOS program she was using to do something with graphs (https://pubmed.ncbi.nlm.nih.gov/20010223/); she found it painfully slow and wondered if I could "make it more user-friendly".

I did my PhD in algorithms at the time and was intrigued when I started reading Greenland, Pearl, and Robins (https://pubmed.ncbi.nlm.nih.gov/9888278/) and then Pearl's "Causality". I soon found that it was not obvious at all how you could speed up that MS-DOS program, and it led to a paper at UAI in 2011 (https://arxiv.org/abs/1202.3764). I made dagitty as a demonstration that you could actually use the algorithms we developed in that paper, and it took off from there -- started with 10 users per day, growing to the hundreds and thousands as causal inference became more popular.

It's now a bit dated, and I don't have as much time anymore to keep it "fresh" as I would like. But I am still grateful and amazed at about how many people I got to know due to this. Highlights included collaborating with Pearl himself on a solution manual for his book "Causal Inference: A Primer" when it first came out, and so many e-mails I got out of the blue from users all over the world. Just last summer I stayed at the house of the author of one of the builtin examples in dagitty.

As these 14 years flew by, I now am happy to do play a small part in supporting the next generation of causal inference software -- if you're interested in causal inference, be sure to check out pgmpy.org, a Python library for Bayesian networks that includes several causal inference functions (https://arxiv.org/abs/2304.08639). Ankur, the author, did his PhD with me and will soon defend his thesis!

Also, R users, be sure to check out ggdag, a great package by Malcolm Barrett that wraps dagitty functionality in a much nicer and tidyverse-compatible way.

DAGitty – draw and analyze causal diagrams