6/20: Trump rally in Tulsa without social distancing.
6/29: Herman Cain tests positive for COVID-19.
7/30: RIP.
************************
In stats classes we all learn that correlation does not imply causation.
That’s always true in cross-sectional analysis.
But it’s not quite so always true in time-series analysis.
And, do note most importantly that the converse is true: causation does imply correlation (although you might have to correct for a lot of interference from other data to uncover that).
Going back to Bertrand Russell (and others), before there was a field of time-series analysis, it was established that temporal ordering was an important feature of causality: the cause must precede the effect.
Correlation between past events that could theoretically be causal, and future events that could theoretically be effects, is known as prima facia causality.†
And it’s a substantially stronger indication of (true) causality than mere correlation.
In this case, the 9 days between the rally and the positive test is right in the middle of the current CDC range for exposure to onset of symptoms.
† My 1992 dissertation did quite a bit of work on this in the context of monetary policy.
No comments:
Post a Comment