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Learning from Pairwise Marginal Independencies | | Johannes Textor
; Alexander Idelberger
; Maciej Liśkiewicz
; | | Date: |
2 Aug 2015 | | Abstract: | We consider graphs that represent pairwise marginal independencies amongst a
set of variables (for instance, the zero entries of a covariance matrix for
normal data). We characterize the directed acyclic graphs (DAGs) that
faithfully explain a given set of independencies, and derive algorithms to
efficiently enumerate such structures. Our results map out the space of
faithful causal models for a given set of pairwise marginal independence
relations. This allows us to show the extent to which causal inference is
possible without using conditional independence tests. | | Source: | arXiv, 1508.0280 | | Services: | Forum | Review | PDF | Favorites | |
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