网络干扰实验中的治疗分配优化
Optimal Experimental Design for Network Experiments under Interference
AI总结：
针对网络干扰下治疗分配优化问题，提出感知网络的治疗分配框架，开发高效局部搜索算法，经模拟及真实网络验证，该方法可优化实验设计并提升实际应用优势。
AI中文摘要：
网络干扰下的实验设计颇具挑战性，因为实验结果可能取决于相邻单元的治疗分配情况。现有方法虽考虑了网络结构，但通常在小型或简化网络上进行评估，限制了其在复杂现实场景中的适用性。我们提出了一种感知网络的治疗分配框架，该框架通过基于费希尔信息矩阵的最优准则，同时考虑分配平衡与网络拓扑。为解决由此产生的组合优化问题，我们开发了一种可扩展至大型网络的高效局部搜索算法。我们还研究了所得设计的因果属性，考察了存在干扰时总、直接及间接治疗效应的估计情况。在包括 Erdős–Rényi、几何随机图、优先连接及随机块模型在内的多种随机图模型上开展的模拟研究，阐明了网络拓扑如何影响最优治疗分配。将其应用于大学宿舍网络与 ego-Facebook 网络，证明了感知拓扑的实验设计具有实际优势。
英文摘要：
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.
