Constructing Separators and Adjustment Sets in Ancestral Graphs

Benito van der Zander; Maciej Liskiewicz; Johannes Textor

Constructing Separators and Adjustment Sets in Ancestral Graphs

Benito van der Zander, Maciej Liskiewicz, Johannes Textor

Institut für Theoretische Informatik

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.

Originalsprache	Englisch
Titel	Proceedings of the UAI 2014 Conference on Causal Inference: Learning and Prediction - Volume 1274
Seitenumfang	14
Band	1274
Erscheinungsort	Aachen, Germany, Germany
Herausgeber (Verlag)	CEUR-WS.org
Erscheinungsdatum	27.07.2014
Seiten	11-24
Publikationsstatus	Veröffentlicht - 27.07.2014
Veranstaltung	UAI'14 - Quebec, Kanada Dauer: 23.07.2014 → 27.07.2014 http://auai.org/uai2014/

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title = "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.",

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year = "2014",

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booktitle = "Proceedings of the UAI 2014 Conference on Causal Inference: Learning and Prediction - Volume 1274",

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Constructing Separators and Adjustment Sets in Ancestral Graphs. / van der Zander, Benito; Liskiewicz, Maciej; Textor, Johannes.
Proceedings of the UAI 2014 Conference on Causal Inference: Learning and Prediction - Volume 1274. Band 1274 Aachen, Germany, Germany: CEUR-WS.org, 2014. S. 11-24 (CI'14).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Constructing Separators and Adjustment Sets in Ancestral Graphs

AU - van der Zander, Benito

AU - Liskiewicz, Maciej

AU - Textor, Johannes

PY - 2014/7/27

Y1 - 2014/7/27

N2 - 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.

AB - 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.

M3 - Conference contribution

VL - 1274

T3 - CI'14

SP - 11

EP - 24

BT - Proceedings of the UAI 2014 Conference on Causal Inference: Learning and Prediction - Volume 1274

PB - CEUR-WS.org

CY - Aachen, Germany, Germany

T2 - UAI'14

Y2 - 23 July 2014 through 27 July 2014

ER -

Constructing Separators and Adjustment Sets in Ancestral Graphs

Abstract

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