  In settings with interference, researchers commonly define estimands using
exposure mappings to summarize neighborhood variation in treatment assignments.
This paper studies the causal interpretation of these estimands under weak
restrictions on interference. We demonstrate that the estimands can exhibit
unpalatable sign reversals under conventional identification conditions. This
motivates the formulation of sign preservation criteria for causal
interpretability. To satisfy preferred criteria, it is necessary to impose
restrictions on interference, either in potential outcomes or selection into
treatment. We provide sufficient conditions and show that they can be satisfied
by nonparametric models with interference in both the outcome and selection
stages.
