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.

翻译：暴露映射有助于在实验对象之间存在交互作用时研究复杂的因果效应。当前方法要求实验者使用相同的暴露映射来定义感兴趣的效应并施加关于干扰结构的假设。然而，这两种角色在实践中很少一致，实验者被迫做出往往存疑的假设，即其暴露映射是正确的。本文论证，当前暴露映射所承担的两种角色可以且通常应该被分离，使得暴露映射用于定义效应，而不必假设它们捕捉了实验中的完整因果结构。本文通过提供在暴露映射被错误指定时仍能精确估计暴露效应的条件，展示了这一方法在实践中的可行性。一些重要问题仍有待研究。