Causality
Pearl showed that correlation and cause can be told apart formally, given a separate operator for intervening rather than only observing.
Why it matters
The question of what happens if one intervenes acquired a calculus, which statistics had avoided for a century.
The book introduces the do operator, the back-door criterion, and the means of deciding whether a causal effect is estimable from available data. It answers a long-standing prohibition on speaking of causes in statistics. For machine learning the consequence is direct: a model that has learned correlations does not know what happens when conditions change. Pearl received the 2011 Turing Award for this line.