Statistical Modeling, Causal Inference, and Social Science: The answer is poststratification

The answer is poststratification

Kaiser writes,

ZBicyclist

Another simple way of explaining this to lay people:

The model gives you a prediction for each of these individual cases:
male A
male B
female A
female B
so, to get the overall population effect, we simply look at the proportion of the population who will fall into each of these 4 buckets.

This is similar to Andrew's last paragraph, but I find "age adjusting" to be a mysterious concept to some people. They tend to think it's a more sophisticated/magical technique than it is.

current grad student

It probably only seems more sophicated/magical than it is because of how statistics is often taught. If you only teach people how to use statistical software and then "interpret" the output, that is all they will be able to do!

Learning "this is the estimated treatment effect after adjusting for the effect of age" is not very englightening if you don't even understand how you adjusted for the effect of age!

Anyway, back to Kaiser's question, if you mostly give males treatment A, and females treatment B, how are you going to be able to disentangle the effect of gender from the treatment effect?

Kaiser

grad student: that's why I said "mostly" not exclusively. it's an unbalanced design. not the best, I know but in practice, there are non-statistical reasons why we have to design it like that.