AI RESEARCH
Nonlinear Causal Discovery through a Sequential Edge Orientation Approach
arXiv CS.LG
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ArXi:2506.05590v3 Announce Type: replace-cross Recent advances have established the identifiability of a directed acyclic graph (DAG) under additive noise models (ANMs), spurring the development of various causal discovery methods. However, most existing methods make restrictive model assumptions, rely heavily on general independence tests, or require substantial computational time.