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?

Not exactly a meta-reference, but I have been working on a causal inference library, zEpid. It is mainly from the epidemiology perspective, since that is what my training is in. (I also disagree that causal inference is "mostly connected to econometrics").
My goal was to develop a cohesive library of the various estimators common in epidemiology, as well as other common tools. So you can build a DAG, estimate causal effects for time-fixed exposures, there is some support for time-varying exposures, and other more general functionalities. I also have tried to provide detailed tutorials that demonstrate the code and motivate the ideas behind the approaches. Most of them are located here.
As Dimitriy said, there are several good books that have code available. Another book to consider is What If. I also demonstrate the examples from that book here (there is also code linked on their site).
I used to keep more up-to-date with the other libraries, but have fallen behind. I think a resource that compares some of the available libraries, but I am unaware of such a thing. One major difference between the libraries is the field they come from. For example, I don't have any support for IV analyses, but econ causal inference libraries have much more support. There is also different value judgements from each of the fields about what quantities are important and what methods should be considered. A good starting place would be the field your background is closest to.
