I wish there were an "engineering" version, outlining the steps necessary to get a practical calculation done, with some motivation along the way, outline of applicability, lots of examples and less emphasis on proofs. For example, various statistical tests (z, t, chi-squared, ...) are a part of the standard toolkit of any practitioner. What tools are needed to resolve the Simpson's paradox in the examples given? Is it even the right question to ask?
Michael Nielsen has posted a long essay explaining his understanding of the Pearlean causal DAG model. I don't understand more than half, but that's much more than I got out of a few other papers. Strongly recommended for anyone interested in the topic.