Miguel A. Hernán, John Hsu, and Brian Healy

For much of the recent history of science, learning from data was the academic realm of statistics,^1,2 but in the early 20th century, the founders of modern statistics made a momentous decision about what could and could not be learned from data: They proclaimed that statistics could be applied to make causal inferences when using data from randomized experiments, but not when using nonexperimental (observational) data.^3,4,5 This decision classified an entire class of scientific questions in the health and social sciences as not amenable to formal quantitative inference.

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