  Steady-state models which have been learned from historical operational data
may be unfit for model-based optimization unless correlations in the training
data which are introduced by control are accounted for. Using recent results
from work on structural dynamical causal models, we derive a formula for
adjusting for this control confounding, enabling the estimation of a causal
steady-state model from closed-loop steady-state data. The formula assumes that
the available data have been gathered under some fixed control law. It works by
estimating and taking into account the disturbance which the controller is
trying to counteract, and enables learning from data gathered under both
feedforward and feedback control.
