What do data science and the foundations of quantum theory have to do with one another?

A great deal, it turns out. The particular branch of data science known as causal inference focuses on a problem which is central to disciplines ranging from epidemiology to economics: that of disentangling correlation and causation in statistical data.

Meanwhile, in a slightly different guise, this same problem has been pondered by quantum physicists as part of a continuing effort to make sense of various puzzling quantum phenomena. On top of that, the most celebrated result concerning quantum theory's meaning for the nature of reality – Bell's theorem – can be seen in retrospect to be built on the solution to a particularly challenging problem in causal inference.

Recent efforts to elaborate upon these connections have led to an exciting flow of techniques and insights across the disciplinary divide.

Perimeter researchers Robert Spekkens and Elie Wolfe have done pioneering work studying relations of cause and effect through a quantum foundational lens, and can be counted among a small number of physicists worldwide with expertise in this field.

In their joint webcast from Perimeter on October 7, Spekkens and Wolfe will explore what is happening at the intersection of these two fields and how thinking like a quantum physicist leads to new ways of sussing out cause and effect from correlation patterns in statistical data.

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