Rethinking Causal Discovery Through the Lens of Exchangeability

This article likely explores a novel approach to causal discovery, a field within AI that focuses on identifying cause-and-effect relationships from data. The use of "exchangeability" suggests the authors are leveraging statistical properties related to data symmetry to improve the process of causal inference. The source being ArXiv indicates this is a pre-print or research paper, suggesting a focus on theoretical advancements.