Just to note, shared folders on google drive etc (anything that syncs changes) can be a real nightmare if you do end up collaborating via git & github

Frequentist alternative to Bayesian model averaging: Incorporate all 6 specifications into a superlearner stack, letting the data dictate how much weight each model receives in the ensemble. (Might need a doubly robust estimator/bootstrapping for valid inference). cran.r-project.org/web/packages...

That's great! With MSM's, is it straightforward to incorporate the estimation of the weights and the weighting of the regression model (Y on A*) with those estimated weights?