Humans train robots to complete tasks in one environment, and expect robots to perform those same tasks in new environments. As humans, we know which aspects of the environment (i.e., the state) are relevant to the task. But there are also things that do not matter; e.g., the color of the table or the presence of clutter in the background. Ideally, the robot's policy learns to ignore these irrelevant state components. Achieving this invariance improves generalization: the robot knows not to factor irrelevant variables into its control decisions, making the policy more robust to environment changes. In this paper we therefore propose a self-supervised method to learn a mask which, when multiplied by the observed state, transforms that state into a latent representation that is biased towards relevant elements. Our method -- which we call TransMASK -- can be combined with a variety of imitation learning frameworks (such as diffusion policies) without any additional labels or alterations to the loss function. To achieve this, we recognize that the learned policy updates to better match the human's true policy. This true policy only depends on the relevant parts of the state; hence, as the gradients pass back through the learned policy and our proposed mask, they increase the value for elements that cause the robot to better imitate the human. We can therefore train TransMASK at the same time as we learn the policy. By normalizing the magnitude of each row in TransMASK, we force the mask to align with the Jacobian of the expert policy: columns that correspond to relevant states have large magnitudes, while columns for irrelevant states approach zero magnitude. We compare our approach to other methods that extract relevant states for downstream imitation learning. See our project website: https://collab.me.vt.edu/TransMASK/