Yes, counterfactual inference is relevant to this. But it is not so much about answering "what would have happened if?", but more about control theory and feedback loops: Your model never being a static function, but a node inside a giant recursive net composed of other models and humans.
Another example (this time on the output-end): You build a model to route emails to sets of experts inside an organization. Your proxy loss is multi-class logistic loss on topic classes. You are interested in improving response times (which you can more or less measure in aggregate) and quality of response (which is harder to measure, if at all).
You build a first iteration of the model and response times improve. Then you create new features and modeling techniques and you improve logistic loss, but when you deploy this model, response times go way down. What happened? Maybe the experts fitted/adapted to the model output: They learned how to quickly answer a specific type of email because it keeps getting routed to them. The new model does better matching topics to emails, resulting in those emails now being send to another expert. While this expert in the long-term may become better at answering emails closer to his/her topic expertise, in a faster and more informative manner, in the short-term he/she will be slower and of lower quality, as they need to adapt to the new types of emails they are getting, and lack the priors for dealing with ambiguous emails.
Both on the input and the output of models there are all sorts of these nasty human-feedback loops that are very hard to even identify and harder to solve.
Leon Bottou gave a talk about these challenges in the context of ML at Facebook: http://leon.bottou.org/slides/2challenges/2challenges.pdf (he mentions the decisions of two separate ML teams adversely influencing their individual experiments). This paper (https://research.google.com/pubs/pub43146.html) talks about "hidden feedback loops" and "entanglement".