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. The investigators propose a project that will use causal inference and machine learning methods to answer two important questions:

1. Is bariatric surgery effective for reducing cardiovascular disease and mortality, and if so, for who?
2. Which type of bariatric surgery (gastric bypass or sleeve gastrectomy) is most effective, and for who? The investigators 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. The investigators 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.

Inclusion Criteria:

• Men and women of reproductive age with a diagnosis of obesity, no prior history of cardiovascular disease or cancer, and no contraindications for surgery after 1982

Exclusion Criteria:

• Contraindications to bariatric surgery

A cohort of individuals eligible for bariatric surgery in Sweden