A casual introduction to causal graphs for study design and analysis: what are they, how do we build them, and why should we care?

Abstract: Graphical causal models, such as directed acyclic graphs or DAGs, are a valuable tool for synthesizing the known and unknown relationships relevant to our research questions. Using these tools can help us avoid common biases and errors in study design and data analysis, can help us think through unexpected findings, and can ease the teaching of epidemiologic and statistical methods. Despite extensive mathematical foundations for these methods, however, there remain limited practical resources for the creation and use of DAGs and other causal graphs in applied research. In this talk, Dr Murray will describe the basic principles of DAGs, discuss a variety of approaches to creating a DAG for a specific research question or using a DAG to help identify new questions of interest, and explain the key features of DAGs to consider in study design and analysis.

Bio: Dr Eleanor (Ellie) Murray is an Assistant Professor of Epidemiology at Boston University School of Public Health with expertise in causal inference. Her work focuses on improving methods for evidence-based decision-making and human-data interaction, as well as improving the translation of methodological advances into practical applied work. Application areas include HIV, HPV, cancer, cardiovascular disease, reproductive health research, tuberculosis research, access to care, psychiatric disorders, musculoskeletal disorders, social and environmental epidemiology, and maternal and adolescent health. Dr Murray also conducts meta-research evaluating bias in existing research. She can be found on Twitter @epiellie and is an Associate Editor for Social Media at the American Journal of Epidemiology.