The ever-growing dispersion, which begged Galton for a counterforce, should never have been there in the first place.

human intuition is grounded in causal, not statistical, logic.

“Representation first, acquisition second.”

d-separation. This concept tells us, for any given pattern of paths in the model, what patterns of dependencies we should expect in the data.

The arrows mean only that the probabilities of child nodes are related to the values of parent nodes by a certain formula

Estimand. This is a mathematical formula that can be thought of as a recipe for generating the answer from any hypothetical data, whenever they are available.

A B C. This junction is the simplest example of a “chain,” or of mediation.

How can machines acquire causal knowledge?

One of my goals in this chapter is to explain, from the point of view of causal diagrams, precisely why RCTs allow us to estimate the causal effect X Y without falling prey to confounder bias.