Notice that the path diagram shows every conceivable factor that could affect a baby guinea pig’s pigmentation.

Philip was the first economist to make use of his son’s invention of path diagrams.

However, randomization does have one great advantage: it severs every incoming link to the randomized variable, including the ones we don’t know about or cannot measure (e.g., “Other” factors in Figures 4.4 to 4.6).

A confounder will make A and C statistically correlated even though there is no direct causal link between them.

This is the same law that makes insurance companies so profitable, despite the uncertainties in human affairs.

The computer could not replicate the inferential process of a human expert because the experts themselves were not able to articulate their thinking process within the language provided by the system.

Statisticians have been immensely confused about what variables should and should not be controlled for, so the default practice has been to control for everything one can measure. The vast majority of studies conducted in this day and age subscribe to this practice. It is a convenient, simple procedure to follow, but it is both wasteful and ridden

The rewards of having a causal model that can answer counterfactual questions are immense. Finding out why a blunder occurred allows us to take the right corrective measures in the future. Finding out why a treatment worked on some people and not on others can lead to a new cure for a disease. Answering the question “What if things had been

Perhaps without realizing it, we deal with exceptions to rules and uncertainties in evidence all the time.