|Top panel of three, see first link. Year axis in on bottom panel.|
As we can see, the rate of gliomas has remained essentially unchanged even while cell phone use was skyrocketing. The exponential curve is where we would expect to find glioma rates if we took the predictions of the Swedish study as, well, predictions.
Should this novel approach be applied to other studies, especially those based not on data (whether case control or observational) but on "data" produced by "models"? Will the idea catch on? What a notion!
A Note on Bacon
Make three tables:
- Table 1: Cases in which Y always occurs.
- Table 2: Cases in which Y does not occur.
- Table 3: (if appropriate) Cases in which Y occurs more or less.
Study each list diligently and make a list made of possible factors appearing in each table.
X is to be a cause of Y iff
- It appears in every case on Table 1
- It appears in no case on Table 2
- It appears in greater intensity in cases of Table 3 where Y is greater.
Of course, life is a bit more complex than Bacon and the Scientific Revolutionaries supposed and Bacon's approach suffers a bit from monocausalitis. If a football game is complicated by the presence of the other team, causal analysis is complicated by the presence of Other Causes, some of which act in consort with and others in opposition to our X, so that the pure X-Y relationship always gets muddied up.
|Here, the end is swaged out, not in.|
IOW, it ain't over till its over; at least in quality improvement and troubleshooting. It's important to identify the actual physical cause of a thing, not wave hands at reified abstractions like "randomness" or "correlation."