AI RESEARCH
TriOpt: A Scalable Algorithm for Linear Causal Discovery
arXiv CS.LG
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ArXi:2605.17465v1 Announce Type: new Learning causal relations from observational data is challenging because the graph search space grows super-exponentially with the number of variables. Ordering-based methods reduce this space by first identifying the topological ordering, whereas continuous optimization methods explore most likely regions of the space by casting DAG learning as a differentiable objective with an acyclicity constraint.