Abstract
Social policy is a powerful intervention that has the potential to reduce or widen inequities in population health. While studies estimating the causal effect of social policies on health are valuable to policy stakeholders, these studies frequently report unstratified estimates for the total population, even though differential enforcement by sub-unit populations and geographies is common. The analytical decision to report unstratified estimates assumes a single version of the social policy is implemented uniformly across populations; in the presence of biased implementation, these analyses can generate misleading results that impede meaningful policy evaluation. In this commentary, we highlight the importance of considering differential policy effects among subpopulations as a function of poorly defined policy exposure (ie, lack of causal consistency) rather than effect measure modification or mediation. Framing the issue as one of poorly defined policy exposure allows for critical disentangling of the explicit and implicit purposes of a policy.