Predictive models affect the world through inducing a strategic response or reshaping the environment in which they are deployed—a property called performativity. This results in the need to constantly adapt and re-design the model. We show that prediction using only causal features—those that directly affect the prediction target, and not those that are otherwise correlated to the target—can achieve an equilibrium and close this feedback loop. Thus, a causal predictive model does not require any further adaptation after deployment even if it change its environment.