This webinar is part of the Advanced Methods Webinar Series
Deciding which variables to adjust for when addressing causal questions in observational studies can be challenging. For example, lack of adjustment for some variables might lead to sub-optimal control for confounding whereas overadjustment for other variables can in fact introduce bias to a study. In recent years, causal diagrams have become popular tools in epidemiology that can guide researchers in better understanding the causal structure of a study question. Causal diagrams can act as blue prints for variable selection for causal questions and can help researchers better understand the role of different variables and when, if at all, to adjust for them.
This presentation will introduce causal diagrams, how they work and how they can guide researchers in identifying variables such as confounders, colliders and meditators. A number of examples and scenarios will be presented for each type of variable. Strengths and limitations of causal diagrams will also be discussed.
View recorded presentation below.
For additional information about use of Causal Diagrams please refer to this free online course recommended by Dr. Dr. Mahyar Etminan Causal Diagrams: Draw Your Assumptions Before Your Conclusions
Refer to chapters 6 to 8 of this PDF for more information on Causal Diagrams.
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Dr. Mahyar Etminan is a pharmacoepidemiologist and an Associate Professor in the Department of Ophthalmology with an appointment in the Departments of Medicine, Division of Neurology at the University of British Columbia. His areas of research are focused in ocular epidemiology and drug safety using large population-based databases. In recent years, he has taken a keen interest in the use of causal diagrams in the design and evaluation of epidemiologic studies that address a causal question and has a number of publications in this area in the journals Chest, BMJ and Gastroenterology.