B1181
Title: Graphical criteria for efficient total effect estimation in causal linear models
Authors: Emilija Perkovic - University of Washington (United States) [presenting]
Leonard Henckel - ETH Zurich (Switzerland)
Marloes Maathuis - ETH Zurich (Switzerland)
Abstract: Covariate adjustment is commonly used for total causal effect estimation. In recent years, graphical criteria have been developed to identify all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide causal effect estimates of varying accuracies. We introduce a graphical criterion to compare the asymptotic variance provided by certain valid adjustment sets in a causal linear model. We employ this result to develop two further graphical tools. First, we introduce a simple variance reducing pruning procedure for any given valid adjustment set. Second, we give a graphical characterization of a valid adjustment set that provides the optimal asymptotic variance among all valid adjustment sets. Our results depend only on the graphical structure and not on the specific error variances or the edge coefficients of the underlying causal linear model. They can be applied to DAGs, CPDAGs and maximally oriented PDAGs. Furthermore, the pruning procedure can be applied to MAGs and PAGs.
