[Our moral ancestors: determining adjustment sets in causal diagrams with ease]

This article is concerned with the application of causal diagrams (also called DAGs) to the following 2 tasks, which are often faced in epidemiology: (1) a posteriori verification of the adjustment performed in an empirical study; (2) a priori identification of appropriate covariate sets for adjustment during study design. Causal diagram theory provides several methods for solving both of these tasks. However, some of these methods are computationally highly demanding, and thus cannot be carried out by hand and even pose problems for fast modern computers. In order to ease everyday work with causal diagrams, we discuss here the most efficient method known to date for performing the stated tasks. This method is based on the so-called "ancestor moral graph" construction by Lauritzen et al. and enables epidemiologists to solve at ease even large causal diagrams with dozens of variables and associations. Moreover, the presented method is well-suited for implementation in computer software, like it has been done in the DAG program and its graphical counterpart DAGitty.