Constructing Separators and Adjustment Sets in Ancestral Graphs
Abstract
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.
Cite
Text
van der Zander et al. "Constructing Separators and Adjustment Sets in Ancestral Graphs." Conference on Uncertainty in Artificial Intelligence, 2014.Markdown
[van der Zander et al. "Constructing Separators and Adjustment Sets in Ancestral Graphs." Conference on Uncertainty in Artificial Intelligence, 2014.](https://mlanthology.org/uai/2014/vanderzander2014uai-constructing/)BibTeX
@inproceedings{vanderzander2014uai-constructing,
title = {{Constructing Separators and Adjustment Sets in Ancestral Graphs}},
author = {van der Zander, Benito and Liskiewicz, Maciej and Textor, Johannes},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2014},
pages = {11-24},
url = {https://mlanthology.org/uai/2014/vanderzander2014uai-constructing/}
}