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VOLUME 46 | NUMBER 2 | APRIL 2011


Statistics and Causality: Separated to Reunite - Commentary on Bryan Dowd's "Separated at Birth"

Bryan Dowd (2010) should be commended for laying before us the historical roots of the tensions between statisticians and econometricians which, until today, perpetuate the myth that causal inference is somehow confusing, enigmatic, or controversial. While modern analysis has proven this myth baseless, it is often the historical accounts that put things in the proper perspective.

I see the tension between statistics and economics or, more generally, between statistics and causality, to be rooted in a more fundamental schism than the one portrayed in Dowd's account. Moreover, and contrary to Dowd's narrative, I believe that the schism was justified, necessary, and not sufficiently emphasized. In fact, it was only after the distinction between statistical and causal concepts was made crisp and formal through new mathematical notation that a productive symbiosis has emerged which now benefits both paradigms.

Dowd's account portrays the schism as a product of unfortunate circumstances that could have been avoided, if only the players were more aware of each other work. Economists, we are told, developed causal inference techniques that yield regressional estimates of causal effects (e.g., IV, confounding-control) and, since regression is a proud invention of statistics, there was no reason for statisticians to shun causal analysis as strongly as they did. If they did oppose structural equations, instrumental variables, and observational studies, it must have been due to an unfortunate rhetorical distinction or, perhaps, a fluke in the history of science.

Upon reading Dowd's account on how close statisticians were to develop causal inference techniques by themselves, readers might be tempted to conclude that the distinction between causal and statistical inference is perhaps unwarranted, and that the former is but a nuance of the latter. This would be a mistake and would not cohere with the lesson I draw from the history of causal analysis.

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