  The presence of interference, where the outcome of an individual may depend
on the treatment assignment and behavior of neighboring nodes, can lead to
biased causal effect estimation. Current approaches to network experiment
design focus on limiting interference through cluster-based randomization, in
which clusters are identified using graph clustering, and cluster randomization
dictates the node assignment to treatment and control. However, cluster-based
randomization approaches perform poorly when interference propagates in
cascades, whereby the response of individuals to treatment propagates to their
multi-hop neighbors. When we have knowledge of the cascade seed nodes, we can
leverage this interference structure to mitigate the resulting causal effect
estimation bias. With this goal, we propose a cascade-based network experiment
design that initiates treatment assignment from the cascade seed node and
propagates the assignment to their multi-hop neighbors to limit interference
during cascade growth and thereby reduce the overall causal effect estimation
error. Our extensive experiments on real-world and synthetic datasets
demonstrate that our proposed framework outperforms the existing
state-of-the-art approaches in estimating causal effects in network data.
