Draw your assumptions before your conclusions: Causal diagrams for epidemiologic research
Causal diagrams are used to summarize and communicate researchers’ assumptions about the causal structure of a problem. In the last decade, causal diagrams have helped clarify many apparent paradoxes and describe common biases. As a result, a sound understanding of causal diagrams is becoming increasingly important for practitioners of all disciplines that aim at making causal inferences, including epidemiology. This workshop will provide a non-technical overview of the theory of causal diagrams, its relation to counterfactual theory, and its applications to causal inference. It will also present practical applications of causal diagrams to examples taken from various areas of epidemiologic research, including cardiovascular disease, cancer, infectious diseases, and perinatal epidemiology.
