One of the things I like best about where I work is that there are a lot of smart people. Really, really smart people. I was talking to John K today and he told me about research he’s working on that involves causality — does one thing cause another or not.
Proving that one thing causes another is often difficult. For example, the annual number of people who drowned by falling into a swimming pool from 1999 to 2009 correlates closely with the number of films that actor Nicolas Cage appeared in that year. But correlation does not mean causation, otherwise Cage could decrease pool drownings by retiring.
There’s an interesting notion called Granger Causality. That’s when one time series can be used to make predictions on a second time series that are better than the predictions using only data in the second time series alone.
For example, in the graphs below, beer sales spike on Days 1, 6, 10. That information could be used to improve the predictions for pizza sales.
Granger causality is really more of a statistical thing than cause as in “cause and effect”. Causality is a pretty deep concept.