  Randomized experiments on social networks pose statistical challenges, due to
the possibility of interference between units. We propose new methods for
estimating attributable treatment effects in such settings. The methods do not
require partial interference, but instead require an identifying assumption
that is similar to requiring nonnegative treatment effects. Network or spatial
information can be used to customize the test statistic; in principle, this can
increase power without making assumptions on the data generating process.
