No; I did not fit multiple models. Lasso regression was used to fit a propensity model using the predictors.

Using bachelor's vs. non-bachelor's has advantages in interpretability, so I think this was the right move for my purposes.

I did not spend an exorbitant amount of time investigating diagnostics, for the same reason I used a proprietary package was has been built for running these tests at a production level and has been thoroughly code reviewed. I don't think it's worth the time to construct an overly customized analysis.

Do you have any good textbooks or educational resources to learn these kinds of techniques?

Sure! Though unfortunately most of the stuff comes from scattered lectures, workshops, discussions, book chapters, seminars, papers, etc. But for intro multilevel Bayesian regression in R/STAN I'd say John Kruschke's "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan" and Richard McElreath's "Statistical Rethinking: A Bayesian Course with Examples in R and Stan" would be really solid (Richard also has his course lectures up on youtube if you prefer that, though I found his book super readable, so much so that when I took the class with him a few years back I skipped most of his lectures since the room was really hot. But don't let that dissuade you from watching them, he's a great guy/speaker and quite fun and funny!).

Purely in terms of building my own intuitions/understanding, though, I've found little more helpful than just looking up the relevant algorithms and implementing the damn things from scratch (to talk of reinventing square wheels above lol... though ofc you'd use the far superior underlying code others have written for your actual analysis).