This year’s Nobel prize for economics, which we are supposed to call the “Sveriges Riksbank Prize in Economic Sciences”, is for a method of identifying natural experiments in data. The importance of this is to get over the “correlation is not causation” hump and actually be able to make some statements about causation. In my very simplistic understanding, you would find at least two data sets where at least two variables are correlated in one but not the other, and the state of the system they represent is roughly the same except for one other variable. Then you can infer that other variable had some role in causing the correlation or lack thereof. This is how you design an experiment of course, but in this case you are looking in existing data sets for cases where this occurred “naturally”.

That’s my simplistic understanding. Let’s look at how Nature describes it.

In 1994, Angrist and Imbens developed a mathematical formalization for extracting reliable information about causation from natural experiments, even if their ‘design’ is limited and compromised by unknown circumstances such as incomplete compliance by participants³. Their approach showed which causal conclusions could and could not be supported in a given situation.

Nature

It seems like this would have applications well beyond economics and social science. For example, ecology, and environmental science in general, where there are just so many variables and complex interactions that setting up randomized controlled experiments in daunting. (Although it can be done – in ecological microcosms, for example). It must have evil applications too of course, from advertising to politics.