Learning from Pairwise Marginal Independencies

2015/08/02 by Johannes Textor, Textor, Johannes, Alexander Idelberger +3
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Advanced Graph Neural Networks #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences

paper · pdf · doi:10.48550/arxiv.1508.00280

openalex publication_date 2015/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective
• Kernel-based Conditional Independence Test and Application in Causal Discovery
• Estimating high-dimensional directed acyclic graphs with the PC-algorithm

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