  In settings where interference between units is possible, we define the
prevalence of indirect effects to be the number of units who are affected by
the treatment of others. This quantity does not fully identify an indirect
effect, but may be used to show whether such effects are widely prevalent.
Given a randomized experiment with binary-valued outcomes, methods are
presented for conservative point estimation and one-sided interval estimation.
No assumptions beyond randomization of treatment are required, allowing for
usage in settings where models or assumptions on interference might be
questionable. To show asymptotic coverage of our intervals in settings not
covered by existing results, we provide a central limit theorem that combines
local dependence and sampling without replacement. Consistency and minimax
properties of the point estimator are shown as well. The approach is
demonstrated on an experiment in which students were treated for a highly
transmissible parasitic infection, for which we find that a significant
fraction of students were affected by the treatment of schools other than their
own.
