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Visual Revelations: Happiness and Causal Inference

My old and very dear friend Henry Braun describes a statistician as someone who’s pretty good with numbers but hasn’t got the personality to be an accountant. I like the ambiguity of the description, vaguely reminiscent of a sign next to a new housing development near me, “Never so much for so little.” Although ambiguity has an honored place in humor, it is less suitable within science. I believe that although some ambiguity is irreducible, some could be avoided if we could just teach other scientists to think more like statisticians. Let me provide one illustration. Issues of causality have haunted human thinkers for centuries, with the modern view usually ascribed to David Hume. Statisticians Ronald Fisher and Jerzy Neyman began to offer new insights into the topic in the 1920s, but the last 40 years— beginning with Don Rubin’s unlikely sourced 1974 paper—have witnessed an explosion in clarity and explicitness on the connections between science and causal inference. A significant event in statisticians’ modern exploration of this ancient topic was Paul Holland’s comprehensive 1986 paper, Happiness and causal inference