27: Efficient Optimal Design for Experiments on Networks under Interference

Thomas M. Menino Convention & Exhibition Center 

Experimental design on networks is complicated by interference, where outcomes may depend on the treatment assignments of neighboring units. While existing methods account for network structure, they are typically evaluated on small or simplified networks, limiting their relevance for complex real-world settings. We propose a network-aware design framework for treatment allocation that integrates allocation balance with network topology through an optimality criterion based on the Fisher information matrix. An efficient local search algorithm enables scalable optimization over large combinatorial design spaces. We examine the causal properties of the resulting designs by evaluating total, direct, and indirect treatment effects under interference. Simulation studies across multiple random graph models, including Erdös-Rényi, geometric random graphs, preferential attachment models, and stochastic block models, demonstrate how network structure shapes optimal allocations. Applications to real-world networks, e.g., college housing and ego-Facebook networks, demonstrate topology-aligned treatment allocations.

Complex networks

Experimental design

Interference

Heterogeneous causal treatment effect 

Section on Statistical Learning and Data Science