add info about related packages and learning resources · pymc-labs/CausalPy#71

2022-11-17T10:16:46.000Z

## 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

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Description

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

Contributor guide

Research direction: Add the provided packages, books, and blog posts to the documentation page for related resources.
Tech stack: python
Domain: data
Issue type: Documentation
Difficulty: 1
Estimated time: Under 1 hour
Activity status: Active
Clarity: Clear
Prerequisites: None
Newbie friendliness: 90