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][1], 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.

The authors offer an example in a footnote regarding the forecasts on the ongoing COVID-19 pandemic:

> For instance Dr. Fauci’s warning that the number of (verified) infections could reach 100 K per day (New York Times, June 30, 2020) should not be interpreted as a forecast to be judged according to its accuracy; rather a signal about what could happen should one avoid taking action.

  [1]: https://www.sciencedirect.com/science/article/pii/S0169207020301230