In degenerate cases where the target label is deterministic, both label shift and covariate shift assumptions can hold simultaneously, even if the label $y$ causes the input $\mathbf{x}$. Under these overlapping conditions, it is often more advantageous to employ techniques based on the label shift assumption. This preference arises because label shift methods typically involve manipulating low-dimensional label objects, rather than the high-dimensional input features commonly dealt with in deep learning.