We consider graphs that represent pairwise marginal independencies amongst a set of variables (for instance, the zero entries of a covari-ance 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.
|Title of host publication
|The 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015)
|Number of pages
|Published - 02.08.2015
|The 31st Conference on Uncertainty in Artificial Intelligence (UAI'15) - Amsterdam, Netherlands
Duration: 12.07.2015 → 12.07.2015