Uncertainty in Artificial Intelligence
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Constructing Separators and Adjustment Sets in Ancestral Graphs
Benito van der Zander, Maciej Liskiewicz, Johannes Textor
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.
Pages: 907-916
PS Link:
PDF Link: /papers/14/p907-van_der_zander.pdf
@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)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "907--916"

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