Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference

Zahra Fatemi, Jean Pouget-Abadie, Elena Zheleva

Abstract:

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.