It is literally impossible to run these models on data without first understanding these methods deeply, because causal inference for these observational data IS extremely difficult. If you do not understand your model deeply, or if you do not understand your data deeply, you are likely producing garbage. This course relates to the first point.

There's a lot of structural econometric papers that do exactly what you ask, but you need graduate level statistics and a deep understanding of discrete choice, identification and simulation methods.

Structural econometrics is a field where PhD students, in their 5 year of study, usually produce only one complete study, if that.

I let my kids practice with hammers, nails and wood (tools / supplies) before I introduce building a piece of furniture (the educational end-goal). These models are the tools of the trade.

I agree with the sentiment that if work is messy, teaching should have messy as well. But not when you're starting out with new tools.

+1 for the kids tools. To extend, I let my kids practice hammering first, then sawing, then some other skill. Learning to work with messy data can wait until you are used to the new tools.