The Book of Why

The Book of Why

The New Science of Cause and Effect

Judea Pearl and Dana Mackenzie

False Positives

Rare signals drown in false positives.

The conclusion is startling. I think that most forty-year-old women who have a positive mammogram would be astounded to learn that they still have less than a 1 percent chance of having breast cancer. Figure 3.3 might make the reason easier to understand: the tiny number of true positives (i.e., women with breast cancer) is overwhelmed by the number of false positives.
Testable Thinking

Transforming vague concepts into testable frameworks.

Humans must have some compact representation of the information needed in their brains, as well as an effective procedure to interpret each question properly and extract the right answer from the stored representation. To pass the mini-Turing test, therefore, we need to equip machines with a similarly efficient representation and answer-extraction algorithm. Such a representation not only exists but has childlike simplicity: a causal diagram.
Mental Maps

Simplified models systematically fail in complex reality.

While awareness of the need for a causal model has grown by leaps and bounds among the sciences, many researchers in artificial intelligence would like to skip the hard step of constructing or acquiring a causal model and rely solely on data for all cognitive tasks. The hope—and at present, it is usually a silent one—is that the data themselves will guide us to the right answers whenever causal questions come up. I am an outspoken skeptic of this trend because I know how profoundly dumb data are about causes and effects.