Conceptual Aspects of Causal Networks in an Applied Context

Abstract

Making causal inference is conceptually straightforward in the setting of a randomized intervention, such as a clinical trial. However, in observational studies, which represent the majority of most large-scale epidemiologic studies, causal inference is complicated by confounding and lack of clear directionality underlying an observed association. In most large scale biomedical applications, causal inference is embodied in Directed Acyclic Graphs (DAG), which is an illustration of causal relationships (i.e., arrows) among the variables (i.e., nodes). A key concept for making causal inference in the context of observational studies is the assignment mechanism, whereby some individuals are treated and some are not. This perspective provides a structure for thinking about causal networks in the context of the assignment mechanism (AM). Estimation of effect sizes of the observed directed relationships is presented and discussed.