Conor Macdonald

The association between weight, and cardiovascular disease and mortality is well established, however, the causal effect of weight-loss in midlife on these outcomes is less clear. Bariatric surgery results in substantial weight-loss and is an ideal candidate to study the causal effects of weight-loss. We propose a project that willcausal inference and machine learning methods to answer two important questions: 2) Is bariatric surgery effective for reducing cardiovascular disease and mortality, and if so, for who? 3) Which type of bariatric surgery (gastric bypass or sleeve gastrectomy) is most effective, and for who?We will use data from various Swedish registers to identify individuals with obesity who are eligible for bariatric surgery. We will then compare cardiovascular and mortality outcomes among those undergoing different types of bariatric surgery with those receiving non-surgical obesity management using causal inference methods. We will use causal forests and expert knowledge to estimate indiviual treatment effects, and identify the groups of patients who benefit the most from these surgeries.This 3-year project will be undertaken by the CAUSALab, at the Unit of Epidemiology, Karolinska Institutet. The team of co-applicants, have extensive experience in using observational data to obtain causal inferences, particularly in the field of cardiovascular disease. A postdoctoral researcher will be hired to work full-time on this project.