Pearl's Causality is very high on my "re-read while making flashcards" list. It is depressing how hard it is to establish causality, but also inspiring how causality can be teased out of observational statistics provided one dares assume a model on which variables and correlations are meaningful.
"provided one dares assume ..." - that's a great quote which I'll steal in the future if you allow!
Most things we learn about DAGs and causality are frustrating, but simulating a DAG (e.g. with lavaan in R) is a technique that actually helps in understanding when and how those assumptions make sense. That's (to me) a key part of making causality productive.
