 Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal . For practical inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter.
<meaning-changed>  Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal . For practical inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter.
 Despite its generality, inference in a single-network setting is still possible under ANI, as we prove that standard inverse-probability weighting estimators can consistently estimate treatment and spillover effects and are asymptotically normal under asymptotics taking the network size large. For inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter.