AI RESEARCH
Coarsening Causal DAG Models
arXiv CS.LG
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ArXi:2601.10531v2 Announce Type: replace-cross 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 a particular dataset. There is a growing body of research on causal abstraction to address such problems.