Although multilevel studies help to tease apart contextual from compositional influences on health, they do not in themselves consider other threats to causal inference, particularly selection and endogeneity.¹ Endogeneity occurs when people choose to move to a particular neighbourhood—for example, one with cleaner air or medical amenities—because of an existing health problem (reverse causation). Endogeneity can also occur because of the presence of unobserved common prior causes of neighbourhood-level exposures and health outcomes (confounding)—for example, it is commonly supposed that the presence of fast-food outlets in a neighbourhood increases the risk of obesity for local residents. However, it is equally plausible that the decision of fast food franchises to open their businesses in particular locations occurs in response to the tastes of local residents. In this instance, taste for fatty food is an unobserved variable that is related to both the location of outlets as well as the risk of obesity. Generally speaking, epidemiological studies to date have seldom attempted to deal with these threats to causal inference.

Arguably, the problems we have described could be overcome by collecting data on a comprehensive range of unobserved variables and controlling for them. Alternatively, analysts could overcome some of the limitations of observational data by importing methods developed in other social sciences, such as instrumental variable estimation.² Instrumental variable estimation has long been used in economics. The goal is to manipulate the exposure of interest (eg, neighbourhood poverty) by identifying variables (instruments) that cause exogenous …