I think usually the approach is estimate the posterior distribution of some outcomes, and use that to make optimal decision based on some loss function. @twiecki and @RavinKumar wrote a bit more in https://twiecki.io/blog/2019/01/14/supply_chain/.
I am not sure optimizing the decision loss during the modeling would make sense, as the loss is not directly observed usually - but it would probably works if we also have historical decision and loss to calibrate the model.