Causal inference in python - where to start? Point 1: I'm not sure if this question could be asked here, as it is may not seem to be about the "science" itself at the first glance! At the second glance though, I think in practice several newbies would face this question and it is a public benefit to have it for reference of people
Point 2: If the community think that this is an inappropriate place for this question, I would delete the whole question when I get an answer. Otherwise, I would edit the question and remove these two points and let the question be here for the reference of others.
So I'm new to the world of causal inference. I am learning some elementary concepts such as DAG, matching on covariates/propensity score, etc. The problem is that I don't know where to start in terms of available packages in python? I have found several packages DoWhy, EconML, Causalib, CausalML, CausalNex, etc... .
The point is that I don't know where to start for these simple tasks such as building a DAG, or matching in python? Does anyone know about a "meta-reference" to know how to work thorough among these libraries?

Here are a few good websites/books that I am fond of that use DAGs, and have code examples in R, Python, and Stata on github or packaged up.

*

*Causal Inference: The
Mixtape and its
github


*Data Analysis for Business, Economics, and
Policy and its
github.


*The Effect, with examples in packages:

*

*install.packages('causaldata') in R

*ssc install causaldata in Stata

*pip install causaldata in Python.



*Using Python for Introductory Econometrics by Florian Heiss and Daniel Brunner.
This is not exactly the cutting-edge stuff, but the foundation you need to get started.
I am an economist at a tech company who uses and teaches these methods.
