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