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in deep learning, particularly the development of deep generative models(DGMs), have spawned a multitude of SCMs, the socalled DSCMs [Pawlowski et al., 2020], which rely on deeplearning approaches to model causal mechanisms. DGMs enable one to build more expressive SCMs [Xia et al., 2021]thanks to the universal approximation capabilities of neuralnetworks [Hornik, 1991]. As a result, they have been used invarious high-stakes domains, including healthcare and fairness, where identifiability guarantees (i.e., existence anduniqueness) of the sought model are fundamental. However,it is essential to acknowledge that different DSCMs may operate under diverse assumptions regarding the functional formof causal mechanisms, leading to distinct guarantees. Understanding these variations is pivotal for informed and effectiveapplication of DSCMs in different contexts.This paper aims to clarify th
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