Constructing Separators and Adjustment Sets in Ancestral Graphs


Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence of latent confounders, and can be inferred from data. Here, we present an algorithmic framework for efficiently testing, constructing, and enumerating m-separators in AGs. Moreover, we present a new constructive criterion for covariate adjustment in directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs) that characterizes adjustment sets as m-separators in a subgraph. Jointly, these results allow to find all adjustment sets that can identify a desired causal effect with multivariate exposures and outcomes in the presence of latent confounding. Our results generalize and improve upon several existing solutions for special cases of these problems.
Original languageEnglish
Title of host publicationProceedings of the UAI 2014 Conference on Causal Inference: Learning and Prediction - Volume 1274
Number of pages14
Place of PublicationAachen, Germany, Germany
Publication date27.07.2014
Publication statusPublished - 27.07.2014
EventUAI'14 - Quebec, Canada
Duration: 23.07.201427.07.2014


Dive into the research topics of 'Constructing Separators and Adjustment Sets in Ancestral Graphs'. Together they form a unique fingerprint.

Cite this