Why did I love this book?
When our descendants look back and ask, “Which scientist’s work changed the way we think, around the year 2000?”, I am prepared to bet that Judea Pearl will be top of the list. Before Pearl, statisticians refused to allow any model of the world into their analysis, thinking it wise to say “correlation does not imply causation,” while remaining scrupulously blind to the reasonableness of some models over others. But the fact that cockerels crow at dawn really is evidence that sunrise causes crowing, and does not constitute any kind of evidence that crowing causes sunrise.
By including such background knowledge in a systematic, graph based manner, Pearl has developed an operational definition of “causation”. This helps to clarify what big data can and cannot deliver, and provides a methodology for establishing the strength of causal connections where we cannot conduct blind trials (like with smoking, or exercise). A very readable, popular science guide to an epoch-defining set of insights.