Heavy going, marred pedagogically by poor choice of notation, too many typographical errors, and overly-complex examples — yet nonetheless **Causal Inference in Statistics: A Primer** by Judea Pearl, Madelyn Glymour, and Nicholas Jewell offers an introductory glimpse of how to think about * cause* and not just

...There is a powerful symbolic machinery, called the

do-calculus, that allows analysis of such intricate structures. In fact, thedo-calculus uncoversallcausal effects that can be identified from a given graph. Unfortunately, it is beyond the scope of this book ...

Perhaps there's a gentler pre-primer introduction to this topic? Perhaps it is yet to be written? Or perhaps Robert Tucci's 2013 "Introduction to Judea Pearl's Do-Calculus" is one such gateway, as recommended by Ferenc Huszár's "ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus" earlier this year?

*(cf Statistics - A Bayesian Perspective (2010-08-13), Introduction to Bayesian Statistics (2010-11-20), Doing Bayesian Data Analysis (2013-11-02), Probability Theory, the Logic of Science (2013-11-18), Statistical Hypothesis Inference Testing (2013-12-01), ...)* - * ^z* - 2018-09-16