Tag: dag
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Coarsening Causal DAG Models
Coarsening Causal DAG Models arXiv:2601.10531v1 Announce Type: new Abstract: Directed acyclic graphical (DAG) models are a powerful tool for representing causal relationships among jointly distributed random variables, especially concerning data from across different experimental settings. However, it is not always practical or desirable to estimate a causal model at the granularity of given features in…
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Nonlinear Causal Discovery through a Sequential Edge Orientation Approach
Nonlinear Causal Discovery through a Sequential Edge Orientation Approach arXiv:2506.05590v1 Announce Type: new Abstract: 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…