Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective

Johannes Textor, Maciej Liskiewicz


Identifying and controlling bias is a key problem in empirical sciences. Causal diagram theory provides graphical criteria for deciding whether and how causal effects can be identified from observed (nonexperimental) data by covariate adjustment. Here we prove equivalences between existing as well as new criteria for adjustment and we provide a new simplified but still equivalent notion of d-separation. These lead to efficient algorithms for two important tasks in causal diagram analysis: (1) listing minimal covariate adjustments (with polynomial delay); and (2) identifying the subdiagram involved in biasing paths (in linear time). Our results improve upon existing exponential-time solutions for these problems, enabling users to assess the effects of covariate adjustment on diagrams with tens to hundreds of variables interactively in real time.
Original languageEnglish
Title of host publicationUAI'11 Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence
EditorsFabio Cozman, Avi Pfeffer
Number of pages28
Place of PublicationArlington, Virginia, USA
PublisherAUAI Press
Publication date14.02.2012
ISBN (Print)ISBN: 978-0-9749039-7-2
Publication statusPublished - 14.02.2012
EventTwenty-Seventh Conference on Uncertainty in Artificial Intelligence - Barcelona, Spain
Duration: 14.07.201117.07.2011


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