By Erica Moodie (William Dawson Scholar & Associate Professor, Department of Epidemiology, Biostatistics, and Occupational Health, Faculty of Medicine, McGill). A significant proportion of research seeks to discover causes – causes of disease, of disparities, of longevity. Causation has been considered by philosophers, religious scholars, mathematicians, for millennia but only recently in ways that are amenable to study with the larger datasets that are available to researchers today. In the last several decades, statisticians have developed a framework to help guide data analyses that can help to understand whether observed relationships learned from data may be “real” or not. I will touch on some historical views of causation, and explain an approach that is used by statisticians to design valid analyses.