Book or article recommendation about causality and counterfactuals I'd like to assign undergraduate students with little to no math experience an article, short part of a book, or even a blog post about causality and counterfactual logic that is easy to understand. 
It seems that most articles and books are too advanced for undergraduate students unfamiliar with formal logic or statistics.
 A: Two longer recommendations that might fit the bill (depending on exactly what no math experience means):


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*Causal Inference in Statistics: A Primer by Judea Pearl,‎ Madelyn Glymour, and ‎ Nicholas P. Jewell - a short book that covers the basic problem, with the first chapter covering basic probability concepts 

*Scott Cunningham's Causal Inference: A Mixtape - a draft of a book with lots of great empirical examples done in Stata. Probably less demanding than the former. 

A: Pearl's The Book of Why is coming out soon, it might be an interesting read for students without background in math but that want to get started in causality.  The Epilogue in Causality (The Art and Science of Cause and Effect) has a nice philosophical overview of the topic and is also a good read.
A: I recommend the chapter "Causal Inference" of Larry Wasserman's All of Statistics. It's 13 pages long, it has a few exercises, and it covers the counterfactual approach to causal modeling in a way that only requires basic knowledge of mathematical statistics (e.g., conditional probability). As a bonus, the following chapter covers the DAG approach to causal models, if you want to throw that in.
