Is it possible to use propensity score matching (or something similar) to create a control group if you do not yet have outcome data? I am looking into using PSM to help determine the effects that an academic intervention has on student dropout rates, and I have a question about how to use it.
Currently, I have information on whether a student was in the treatment, and some demographic information that I want to use as covariates. I do not yet have information on whether or not students dropped out.
I am trying to do as much of my analyses upfront as I can. I would like to create a control group that I can compare to my treatment group in the future, when I have drop out data. Is that possible with PSM? Or do I need to wait to get the dropout data before I can do PSM?

It's not strictly pertinent to your question, but without outcome data you can also use unsupervised machine learning techniques (eg clustering) to get better insights on your study population, and potentially inform patient characterization.
