# Causal modeling and DAGs in Python - where to start and what are the best sources?

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:

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!

• Are you just trying to get a model to play around with? Or are you trying to model a real-world scenario? I'd imagine living on Tatooine, it might be difficult to relate to those of us here on earth. ;-) Oct 27, 2021 at 15:34
• Ah, I see. We'd better work quickly before you turn to the Dark Side. When you get to the specifics of DAGs, you simply ask yourself, "Does this variable affect that one?" of every variable-variable pair. That's usually the easier part, though it can be highly non-trivial. Pearl's book Causality: Models, Reasoning, and Inference has an algorithm that can help discover DAGs based on real-world data. Getting the Structural Causal Models is harder, though. Your best bet is domain experts: ask them and see what they come up with. Oct 27, 2021 at 15:44
• @AdrianKeister, many thanks. I have this book as well his other books. But they are tough as laser sword of Darth Vader. Do I understand correctly that DAG is a visualization and I need to build a model behind, controlling for some variables. Do you have any good tips about resources? I can imagine I can build a regression model, but not sure because I do not see some intuitive tutorials. This might help, no? theeffectbook.net/the-toolbox.html Oct 27, 2021 at 15:50
• Yes, the DAG is a visualization, and it goes hand-in-hand with a SCM, although there are MANY things you can deduce about a system merely from its DAG. I also found Pearl's Causality book extremely difficult - I've only been able to force my way through a small part of it. But he has easier books that might help. I recommend reading these, in order: The Book of Why, and Causal Inference in Statistics: A Primer. That's a great link you have. Definitely might help as well. Oct 27, 2021 at 15:54
• @AdrianKeister, thanks, I am re-reading The Book of Why right now. It is a mandatory reading on Tatooine. Very intuitive. I also tried Primer. Have you seen any full stack tutorials how to go from a DAG to a model and its evaluation, ideally in Python. I have not. Only some disaggregated pieces. Oct 27, 2021 at 15:57