Uncertainty in Artificial Intelligence
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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.
Pages: 681-688
PS Link:
PDF Link: /papers/11/p681-textor.pdf
AUTHOR = "Johannes Textor and Maciej Liskiewicz",
TITLE = "Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "681--688"

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