Covariate shift is a category of distribution shift where the marginal distribution of the input features (covariates) changes over time, while the conditional distribution of the labels given the inputs, $P(y \mid \mathbf{x})$, remains constant. It is the most natural assumption to make in settings where the input features $\mathbf{x}$ are believed to cause the label $y$. An example of covariate shift is training a classifier to distinguish cats and dogs using real photographs, but testing it exclusively on cartoon images.