 For practical inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter. Finally, we illustrate our results in a simulation study and empirical application .
<meaning-changed>  For practical inference, we propose a new conservative variance estimatorbased on a network bootstrap and suggest a data-dependent bandwidth using the network diameter. Finally, we illustrate our results in a simulation study and empirical application .
 For practical inference, we consider a network HAC variance estimator. Under a finite population model, we show the estimator is biased but that the bias can be interpreted as the variance of unit-level exposure effects. This generalizes Neyman's well-known result on conservative variance estimation to settings with interference .