Leveraging Cluster Causal Diagrams for Determining Causal Effects in Medicine

Causal inference, or the task of estimating the causal effect of an exposure or interventional variable on an outcome from an observational dataset, requires precise and rigorous methods, based on assumptions about the system under study. Such assumptions can be articulated as a causal diagram, however use of this technique in medicine is uncommon due to challenges in causal diagram construction in high-dimensional settings. Recent introduction of cluster causal diagrams or C-DAGs promise to ease the task of diagram construction by allowing for the representation of some unknown or partially defined relationships. We evaluate the practical application of C-DAGs in simulated medical contexts. We estimate causal effects under varying sets of assumptions, determined by both causal diagrams and C-DAGs and compare our results. Our findings show empirically similar results, with little discrepancy between causal effect sizes or variance across experimental runs, although estimation and efficiency challenges remain to be explored.