Then the opposite is true: In other words, when evaluating an intervention in a causal model, we make the minimum changes possible to enforce its immediate effect. So we “break” the model where it comes to A but not B.

We should thank the language: I should also mention here that counterfactuals allow us to talk about causality in individual cases: What would have happened to Mr. Smith, who was not vaccinated and died of smallpox, if he had been vaccinated? Such questions, the backbone of personalized medicine, cannot be answered from rung-two information.

Yet we can answer: To be more precise, in geometry, undefined terms like “point” and “line” are primitives. The primitive in causal inference is the relation of “listening to,” indicated by an arrow.