  In many observational studies in social science and medicine, subjects or
units are connected, and one unit's treatment and attributes may affect
another's treatment and outcome, violating the stable unit treatment value
assumption (SUTVA) and resulting in interference. To enable feasible estimation
and inference, many previous works assume exchangeability of interfering units
(neighbors). However, in many applications with distinctive units, interference
is heterogeneous and needs to be modeled explicitly. In this paper, we focus on
the partial interference setting, and only restrict units to be exchangeable
conditional on observable characteristics. Under this framework, we propose
generalized augmented inverse propensity weighted (AIPW) estimators for general
causal estimands that include heterogeneous direct and spillover effects. We
show that they are semiparametric efficient and robust to heterogeneous
interference as well as model misspecifications. We apply our methods to the
Add Health dataset to study the direct effects of alcohol consumption on
academic performance and the spillover effects of parental incarceration on
adolescent well-being.
