Reminds me somewhat of that AirBnB paper [1] around demand estimation for optimal price setting. In both cases the regression target was unobservable. In the instacart case: the counterfactual demand if all delivery options had been available. In the AirBnB case: the counterfactual demand if a house had been listed at a different price. In both cases, it appears the solution was to build bind models and score those to estimate what the demand would have been under the counterfactual case.
[1] https://www.kdd.org/kdd2018/accepted-papers/view/customized-...