A Complete Generalized Adjustment Criterion
Abstract
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.
Cite
@article{arxiv.1507.01524,
title = {A Complete Generalized Adjustment Criterion},
author = {Emilija Perković and Johannes Textor and Markus Kalisch and Marloes H. Maathuis},
journal= {arXiv preprint arXiv:1507.01524},
year = {2015}
}
Comments
10 pages, 6 figures, To appear in Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI2015)