How to correct for sampling bias in one population when comparing against another I have two populations that I'd like to compare across certain metrics. However, most members of population A did not respond to our request for data, and those respondents that did are not representative of the whole.
On the other hand, we have data for every member of population B.
I have trained ML models to determine which characteristics are most important to discern between those members of population A that did respond vs. those that didn't. How can I apply this information to correct for this sampling bias when I compare the data from population A to the data from population B?
My current plan is to apply my ML model to all members of population B and see whether or not we'd predict them to have provided data if they had been members of population A. I'd subset all responses from population B to only those members that are predicted to have provided data. Is this a valid approach?

After further research, I realized this is similar to propensity score matching.
However, I should not use the subset of all members of population B that the ML model predicts as positive responses (i.e. they look similar to members of population A). Instead, I should run all members of both population A and B through the model and match on the "propensity score", which is the probability output of the model.
