add info about related packages and learning resources

[drbenvincent]

Packages

• https://github.com/bernardodionisi/differences
• https://github.com/py-why/dowhy
• https://github.com/amaiya/causalnlp

Books

• [draft] https://alexdeng.github.io/causal/, https://alexdeng.github.io, Alex Deng
• [not yet available book] https://alxndr.io, Aleksander Molak
• [in progress at Manning] https://www.manning.com/books/causal-inference-for-data-science, Aleix Ruiz de Villa
• [in progress at Manning] https://www.manning.com/books/causal-machine-learning, Robert Ness

Top-tier blog posts

• https://emilyriederer.netlify.app/post/causal-design-patterns/, Emily Riederer

[HPCurtis]

Is this still open. Do you have anywhere specific you want this info to be presented?

[drbenvincent]

Still open 👍🏻

Initially I was maybe a bit ambitious. I think we should not try to make this an exhaustive list of causal inference resources. There is already the Awesome Causal Inference curated list by @matteocourthoud.

Instead, maybe we should restrict it to directly related packages and books.

How about a new page under the knowledge base section? We should probably reference the Awesome Causal Inference repo as an example of a more complete list of resources but then go on to list directly relevant resources?

[HPCurtis]

Yeah sounds good. Adding to the section now. Question though do have preference to directly relevant resources. I Know the package was inspired by Quasi-Experimentation: A Guide to Design and Analysis Charles S. Reichardt. Unless im mistaken. So, that's obvious one.