Simple yet Efficient Estimators for Network Causal Inference Even When the Network is Unknown
We propose unbiased estimators for the total treatment effect in settings with network interference. We use a low-degree assumption on the potential outcomes to establish bounds on the variance of our estimators. In settings where the network is unknown, we leverage a staggered rollout experimental design. Beyond our formal guarantees, our estimators are shown to work well in our experiments on simulated data.
May 23, 2022