There is renewed optimism regarding the use of natural experimental studies to generate evidence as to the effectiveness of population health interventions. Natural experimental studies capitalise on environmental and policy events that alter exposure to certain social, economic or environmental factors that influence health. Natural experimental studies can be useful for examining the impact of changes to 'upstream' determinants, which may not be amenable to controlled experiments. However, while natural experiments provide opportunities to generate evidence, they often present certain conceptual and methodological obstacles. Population health interventions that alter the physical or social environment are usually administered broadly across populations and communities. The breadth of these interventions means that variation in exposure, uptake and impact may be complex. Yet many evaluations of natural experiments focus narrowly on identifying suitable 'exposed' and 'unexposed' populations for comparison. In this paper, we discuss conceptual and analytical issues relating to defining and measuring exposure to interventions in this context, including how recent advances in technology may enable researchers to better understand the nature of population exposure to changes in the built environment. We argue that when it is unclear whether populations are exposed to an intervention, it may be advantageous to supplement traditional impact assessments with observational approaches that investigate differing levels of exposure. We suggest that an improved understanding of changes in exposure will assist the investigation of the impact of complex natural experiments in population health.