[axplusb]

Judea Pearl's The Book of Why.

Basically Pearl argues that classical statistics completely ignored the concept of causality so far and introduces a complete framework to bring causal inference into statistical/data analysis. The framework is based on graphs and asks for causal hypotheses (like econometricians would do with instrumental variables) and allows to compute/quantify causal effects.

Anyone working with data should probably read this book. The fact that Pearl brought in a professional math/science writer as co-author is a huge boost to the main ideas accessibility and make for a nice albeit deep summer read.