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Exposure mappings are widely used to model potential outcomes in the presenceof interference, where each unit's outcome may depend not only on its owntreatment, but also on the treatment of other units as well. However, inpractice these models may be only a crude proxy for social dynamics. In thiswork, we give estimands and estimators that are robust to the misspecificationof an exposure model. In the first part, we require the treatment effect to benonnegative (or "monotone") in both direct effects and spillovers. In thesecond part, we consider a weaker estimand ("contrasts attributable totreatment") which makes no restrictions on the interference at all.

Using Exposure Mappings as Side Information in Experiments with Interference