  Exposure mappings facilitate investigations of complex causal effects when
units interact in experiments. Current methods require experimenters to use the
same exposure mappings both to define the effect of interest and to impose
assumptions on the interference structure. However, the two roles rarely
coincide in practice, and experimenters are forced to make the often
questionable assumption that their exposures are correctly specified. This
paper argues that the two roles exposure mappings currently serve can, and
typically should, be separated, so that exposures are used to define effects
without necessarily assuming that they are capturing the complete causal
structure in the experiment. The paper shows that this approach is practically
viable by providing conditions under which exposure effects can be precisely
estimated when the exposures are misspecified. Some important questions remain
open.
