causality-informed quantitative approaches to give us an insight into the relationship between sex (biological differences) and sexism (social and structural inequalities). Causality-informed quantitative approaches are statistical methods that allow us to explore potential cause-and-effect relationships, helping us understand not just whether factors are connected, but how and why those connections might exist.

To do this, we will use methods such as g-methods and formal mediation analysis. Both approaches are based on the potential outcomes framework, which uses observed data to estimate what might have happened under different conditions (also known as ‘counterfactuals’).

We will also use directed acyclic graphs (DAGs) to inform model building and guide our analysis. DAGs are visual maps showing how different factors may be related and will help clarify our assumptions whilst also helping us to identify what needs to be considered when analysing the data.