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.

So far I have went through:

• Basics of Judea Peal and his work
• This cool book by Matheus Facure
• Several MOOCs like UPenn Crash Course and Brady Neal Course
• Python libraries like DoWhy, CausalNex, 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 causal model with a couple of numeric X variables only and 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!

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.

So far I have went through:

• Basics of Judea Peal and his work
• This cool book by Matheus Facure
• Several MOOCs like UPenn Crash Course and Brady Neal Course
• Python libraries like DoWhy, CausalNex, 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!