בדיקת תוצאות ללמידת ייצוגים סיבתיים

תקציר מקורי באנגליתarXiv:2502.20099v3 Announce Type: replace-cross Abstract: We evaluate methods for causal representation learning (CRL) on a simple, real-world system that satisfies the basic problem setup of CRL. The system consists of a controlled optical experiment producing a variety of measurements where the underlying causal factors---the control inputs to the experiment---are known, providing a ground truth. We select methods representative of different approaches to CRL and find that they all fail to consistently recover the underlying causal factors. To understand the failure modes of the evaluated algorithms, we perform an ablation on the data by substituting the real data-generating process with a simpler synthetic equivalent. The results reveal a reproducibility problem, as most methods already