60 research outputs found
A Complete Generalized Adjustment Criterion
Covariate adjustment is a widely used approach to estimate total causal
effects from observational data. Several graphical criteria have been developed
in recent years to identify valid covariates for adjustment from graphical
causal models. These criteria can handle multiple causes, latent confounding,
or partial knowledge of the causal structure; however, their diversity is
confusing and some of them are only sufficient, but not necessary. In this
paper, we present a criterion that is necessary and sufficient for four
different classes of graphical causal models: directed acyclic graphs (DAGs),
maximum ancestral graphs (MAGs), completed partially directed acyclic graphs
(CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the
existing ones and in this way unifies adjustment set construction for a large
set of graph classes.Comment: 10 pages, 6 figures, To appear in Proceedings of the 31st Conference
on Uncertainty in Artificial Intelligence (UAI2015
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