Learning from Pairwise Marginal Independencies

Johannes Textor; Alexander Idelberger; Maciej Liśkiewicz

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arXiv · 1508.00280

Learning from Pairwise Marginal Independencies

Johannes Textor·Alexander Idelberger·Maciej Liśkiewicz

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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.

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Johannes Textor, Alexander Idelberger, Maciej Liśkiewicz. 2015-08-02. Learning from Pairwise Marginal Independencies. https://arxiv.org/abs/1508.00280

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