The interventionist approach can address questions related to causes of effects if causes are considered as states instead of interventions

The interventionist approach to causal inference recommends that observational studies be framed as randomized trials with well-defined interventions to more precisely define the population of interest, exposure comparisons, assignment procedures, follow-up period, and outcomes. However, others suggest causes are not restricted to interventions, and the approach is too restrictive and will limit science. The described examples usually refer to questions about causes of effects/outcomes (rather than effects of interventions), where the 'cause' of interest often represents a mediator variable between an existing or hypothesized intervention. These questions are important because they represent the foundation for improving existing interventions and developing new interventions. In this article, I show how the interventionist approach can be used to try and answer these broader 'causes of effects/outcomes' questions. I use the sufficient casual set framework popularized by Rothman, which considers causes as states rather than interventions. The method is fully consistent with the potential outcomes approach and the need for well-defined counterfactuals. Whereas the effect of an intervention can theoretically be evaluated with an idealized single randomized trial, evaluating the causes of effects/outcomes requires evidence synthesis across multiple studies that each emulate a different target randomized trial. In other words, questions related to the effects of interventions are a special case where all the evidence that needs to be synthesized is available within a single idealized trial. Finally, considering states as causes also provides a transparent way to think about well-defined counterfactuals, and how factors that are commonly referred to as "non-manipulable" such as sex can also be studied as causes.