In the context of cause-effect pairs in machine learning, it is generally assumed that there is no measurement noise. Measurement noise may occur if, for example, altitude is not measured precisely, with its noisy version denoted as $ilde{Z}$. However, the variable temperature $T$ is still a function of the original variable $Z$ that is not corrupted by measurement noise. This is related to a cause-effect pair problem between $T$ and $ilde{Z}$ in the presence of a latent hidden variable, which is the original $Z$.