Understanding the extent to which an intervention ‘works’ can provide compelling evidence to decision-makers, although without an accompanying explanation of how an intervention works, this evidence can be difficult to apply in other settings, ultimately impeding its usefulness in making judicious and evidence-informed decisions.
In this paper, we describe causal chain analysis as involving the development of a logic model, which outlines graphically a hypothesis of how an intervention leads to a change in an outcome. This logic model is then used to anchor subsequent decisions in the systematic review process, including decisions on synthesis.
In this paper, we outline the steps taken in building a logic model, which usually consists of a series of boxes depicting intervention components and processes, outputs, and outcomes with arrows depicting connecting relationships. The nature of these connecting relationships and their basis in causality are considered, through a focus on complex causal relationships and the way in which contextual factors about the intervention setting or population may moderate these. We also explore the way in which specific combinations of intervention components may lead to successful interventions. Evidence synthesis techniques are discussed in the context of causal chain analysis, and their usefulness in exploring different parts of the causal chain or different types of relationship. The approaches outlined in this paper aim to assist systematic reviewers in producing findings that are useful to decision-makers and practitioners, and in turn, help to confirm existing theories or develop entirely new ways of understanding how interventions effect change.