Beyond associations: From theory to interventions in health inequalities research using causal inference

One of the central goals of public health is not only to improve the health in the population overall, but also to reduce the unequal distribution of health and disease within the population. Even though a large amount of research is directed towards identifying and understanding health inequalities, much of this research is based on associations. This type of research can help to identify what groups in society are at risk of having worse health but cannot tell us how these inequalities may be reduced. In order to move beyond identifying who is at risk, we illustrate how we can combine the existing theoretical foundations with the counterfactual outcomes framework to understand how health inequalities can be tackled. We show how the Commission on Social Determinants of Health (CSDH) framework and the Diderichsen model can be translated into practice through the use of DAGs and notation. This will aid in generating more informative evidence on how certain interventions can reduce inequalities.