Problem
Research questions and friction points this paper is trying to address.
network interference
experimental design
treatment allocation
Optimizing Treatment Allocation in Experiments with Network Interference
🤖 AI Summary
研究提出一种网络感知的治疗分配框架，通过优化Fisher信息矩阵来解决网络干扰下的实验设计问题，并开发了适用于大规模网络的高效局部搜索算法。
📝 Abstract
Experimental design under network interference is challenging because outcomes may depend on the treatment assignments of neighboring units. Existing approaches account for network structure but are typically assessed on small or simplified networks, limiting their applicability to complex real-world settings. We propose a network-aware treatment allocation framework that jointly accounts for allocation balance and network topology via an optimality criterion based on the Fisher information matrix. To address the resulting combinatorial optimization problem, we develop an efficient local search algorithm that scales to large networks. We further study the causal properties of the resulting designs by examining the estimation of total, direct, and indirect treatment effects in the presence of interference. Simulation studies across a range of random graph models, including Erdős--Rényi, geometric random graphs, preferential attachment, and stochastic block models, illustrate how network topology influences optimal treatment allocations. Applications to college housing and ego-Facebook networks demonstrate the practical advantages of topology-aware experimental designs.
Innovation
Methods, ideas, or system contributions that make the work stand out.
network-aware treatment allocation
Fisher information matrix
local search algorithm
large networks
causal properties
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