Causal structure learning in directed, possibly cyclic, graphical models
We consider the problem of learning a directed graph G⋆{G}^{\star } from observational data. We assume that the distribution that gives rise to the samples is Markov and faithful to the graph G⋆{G}^{\star } and that there are no unobserved variables. We do not rely on any further assumptions regardi...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-04-01
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| Series: | Journal of Causal Inference |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jci-2024-0037 |
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