  This paper presents a randomization-based framework for estimating causal
effects under interference between units, motivated by challenges that arise in
analyzing experiments on social networks. The framework integrates three
components: (i) an experimental design that defines the probability
distribution of treatment assignments, (ii) a mapping that relates experimental
treatment assignments to exposures received by units in the experiment, and
(iii) estimands that make use of the experiment to answer questions of
substantive interest. We develop the case of estimating average unit-level
causal effects from a randomized experiment with interference of arbitrary but
known form. The resulting estimators are based on inverse probability
weighting. We provide randomization-based variance estimators that account for
the complex clustering that can occur when interference is present. We also
establish consistency and asymptotic normality under local dependence
assumptions. We discuss refinements including covariate-adjusted effect
estimators and ratio estimation. We evaluate empirical performance in realistic
settings with a naturalistic simulation using social network data from American
schools. We then present results from a field experiment on the spread of
anti-conflict norms and behavior among school students.
