One of the essential elements of an impact evaluation is that it not only measures or describes changes that have occurred but also seeks to understand the role of particular interventions (i.e., programmes or policies) in producing these changes. This process is often referred to as causal attribution, causal contribution or causal inference. This brief provides an overview of different ways to examine causal attribution, using a combination of research design and particular data collection and analysis strategies. The OECD-DAC definition of impact makes it clear that an impact evaluation must establish what has been the cause of observed changes: “Positive and negative, primary and secondary long-term effects produced by a development intervention, directly or indirectly, intended or unintended.”1 Causal attribution is defined by OECD-DAC as: “Ascription of a causal link between observed (or expected to be observed) changes and a specific intervention.”2 This definition does not require that changes are produced solely or wholly by the programme or policy under investigation3 . In other words, it takes into consideration that other causes may also have been involved, for example, other programmes/policies in the area of interest or certain contextual factors (often referred to as ‘external factors’). Evaluations produce stronger and more useful findings if they not only investigate the links between activities and impacts but also investigate links along the causal chain between activities, outputs, intermediate outcomes and impacts. A ‘theory of change' 4 (see Brief No. 2, Theory of Change) is therefore helpful in guiding causal attribution in an impact evaluation. The evaluation may confirm the theory of change or it may suggest refinements based on the analysis of evidence.