• For some interventions, tight and testable theories of change are not appropriate – for example, in fast moving humanitarian emergencies or participatory development programmes, a more flexible approach is needed. • However, it is still possible to have a flexible project design and to draw conclusions about causal attribution. This middle path involves ‘loose’ theories of change, where activities and outcomes may be known, but the likely causal links between them are not yet clear. • In this approach, data is collected ‘after the event’ and analysed across and within cases, developing testable models for ‘what works’. More data will likely be needed than for projects with a ‘tight’ theory of change, as there is a wider range of relationships between interventions and outcomes to analyse. The theory of change plays an important role in guiding the selection of data types. • While loose theories of change are useful to identify long term impacts, this approach can also support short cycle learning about the effectiveness of specific activities being implemented within a project’s lifespan.

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