I am afraid that such situations are fundamentally inherent in predicting/forecasting contexts; quoting from the very recent paper by Taleb et al., On single point forecasts for fat-tailed variables (open access, para 3.7):
3.7. Forecasts can result in adjustments that make forecasts less accurate
It is obvious that if forecasts lead to adjustments, and responses that affect the studied phenomenon, then one can no longer judge these forecasts on their subsequent accuracy.
So, other than communicating this clearly beforehand and reaching an agreement on how the predictions will be assessed in the presence of such adjustments, there is not much else you can do from a modelling or methodology perspective. The advice suggested further in the same paragraph quoted above:
In that sense a forecast can be a warning of the style “if you do not act, these are the costs”.
can form the basis of such a communication and agreement.