I am very new to causal models (and econometrics) and need to pick up basics fast. I am comfortable with ML though. I did an extensive research during last several days on causality, DAGs, and modeling, but still not sure how to approach it properly. Having [the same problem as this user][1]. 

So far I have went through:
- Basics of Judea Peal and his work
- This [cool book][2] by Matheus Facure
- Several MOOCs like [UPenn Crash Course][3] and [Brady Neal Course][4]
- Python libraries like [DoWhy][5], [CausalNex][4], and others

But still I do not see enough clear examples of modeling work (or maybe I am looking in a wrong direction). I do not touch Bayesian Networks for now, this is unnecessary for this project. 

What I am trying to do right now:
- I need to build a DAG and **a simple and explainable causal model** with a couple of numeric X variables only and a numeric Y variable. I think I can do it with some libraries above. But if I understand correctly, DAG is just a graphical representation of the relationship and I need a model behind.
- I googled a lot about ML, DAGs, etc, but still either do not understand it well (which is true), looking in a wrong direction). Facure's book is the closest I have seen so far about modeling and evaluation of causal models **(where to read about evaluation more?)**

I would greatly appreciate some tips - feel free to share your favorite resources!


  [1]: https://stats.stackexchange.com/questions/545054/causal-inference-in-python-where-to-start
  [2]: https://matheusfacure.github.io/
  [3]: https://www.coursera.org/learn/crash-course-in-causality
  [4]: https://www.bradyneal.com/causal-inference-course
  [5]: https://microsoft.github.io/dowhy/