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?

From what I see causal inference (CI) is mostly connected to econometrics, and vastly to its most basic concepts. Also it is mostly academic domain, since the companies are usually not interested in any inference at all.
The tools to use in applied CI are often limited to the basic regressions, panels, IV. The DAG's, understanding endogeneity and some other CI concepts complement those tools. There are some sophisticated tricks to help with the identification of the parameters, but the tools themselves are mostly very basic.
The statsmodels package with pandas and something to make charts, like plotly may be more than enough to start and finish your PhD in applied CI.
However, it really depends on what you aim for. CI is quite a big field, and there are many sophisticated areas within it. But from my perspective - they are not the core.
