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with this data? Instead of helping people stay well, we could use such
models to gouge sick people with huge premiums, or we could drop
sick people from insurance altogether.
This is not a modeling question. It's a question of what, as a society,
we want to do with our models.
Modern Academic Statistics
It used to be the case, say 20 years ago, according to Madigan, that
academic statisticians would either sit in their offices proving theo‐
rems with no data in sight—they wouldn't even know how to run a t-
test—or sit around in their offices and dream up a new test, or a new
way of dealing with missing data, or something like that, and then
they'd look around for a dataset to whack with their new method. In
either case, the work of an academic statistician required no domain
expertise.
Nowadays things are different. The top stats journals are more deep
in terms of application areas, the papers involve deep collaborations
with people in social sciences or other applied sciences. Madigan sets
an example by engaging with the medical community.
Madigan went on to make a point about the modern machine learning
community, which he is or was part of: it's a newish academic field,
with conferences and journals, etc., but from his perspective, it's char‐
acterized by what statistics was 20 years ago: invent a method, try it
on datasets. In terms of domain expertise engagement, it's a step back‐
ward instead of forward.
Not to say that statistics are perfect; very few academic statisticians
have serious hacking skills, with Madigan's colleague Mark Hansen
being an unusual counterexample. In Madigan's opinion, statisticians
should not be allowed out of school unless they have such skills.
Medical Literature and Observational Studies
As you may not be surprised to hear, medical journals are
full
of ob‐
servational studies. The results of these studies have a profound effect
on medical practice, on what doctors prescribe, and on what regulators
do.
For example, after reading the paper entitled
“Oral bisphosphonates
