Uncertainty in Artificial Intelligence
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Constructing Separators and Adjustment Sets in Ancestral Graphs
Benito van der Zander, Maciej Liskiewicz, Johannes Textor
Abstract:
Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence of latent confounders, and can be in- ferred from data. Here, we present an algo- rithmic framework for efficiently testing, con- structing, and enumerating m-separators in AGs. Moreover, we present a new constructive crite- rion 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 iden- tify a desired causal effect with multivariate ex- posures and outcomes in the presence of latent confounding. Our results generalize and improve upon several existing solutions for special cases of these problems.
Keywords:
Pages: 907-916
PS Link:
PDF Link: /papers/14/p907-van_der_zander.pdf
BibTex:
@INPROCEEDINGS{van der Zander14,
AUTHOR = "Benito van der Zander and Maciej Liskiewicz and Johannes Textor",
TITLE = "Constructing Separators and Adjustment Sets in Ancestral Graphs",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "907--916"
}


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