According to the central limit theorem, proven in 1810 by Pierre-Simon Laplace, any such random process—one that amounts to a sum of a large number of coin flips—will lead to the same probability distribution, called the normal distribution (or bell-shaped curve). The Galton board is simply a visual demonstration of Laplace’s theorem.

Indeed, the diagram is like the engine of the Bayesian network. But like any engine, a Bayesian network runs on fuel. The fuel is called a conditional probability table.

The recognition that causation is not reducible to probabilities has been very hard-won,

This lack of flexibility and adaptability is inevitable in any system that works at the first level of the Ladder of Causation.

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

language. Linguistic barriers are not surmounted so easily.

How can machines (and people) represent causal knowledge in a way that would enable them to access the necessary information swiftly, answer questions correctly,

probability as a guardian of common sense and merely repair its computational deficiencies.

Computers are not good at breaking rules,