Optimal Block Designs for Experiments on Networks
Optimal Block Designs for Experiments on Networks
复制标题
网络实验的最佳模块设计
DOI：
10.1111/rssc.12473
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Koutra V
中科院分区：
文献类型：
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作者：
Koutra V
我们提出了一种方法来构造最优的区组设计的网络上的实验。针对给定网络干扰结构的响应模型扩展了线性网络效应模型，以包含块。选择的最优性标准，以反映实验目标和交换算法是用来搜索整个设计空间，以获得一个有效的设计时，穷举搜索是不可能的。我们的兴趣在于估计治疗之间的直接比较，在滋扰网络效应的存在下，源于底层网络干扰结构的实验单位，或在网络效应本身。通过比较处理效应估计量的方差和偏倚，对不同模型（包括标准处理模型）下的最优设计进行了比较。我们还提出了一种定义块的方法，同时考虑到网络内实验单元组的相互关系，使用谱聚类技术来实现最佳模块化。我们期望封闭形式社区中的连接单元的行为与外部刺激相似。我们提供的证据表明，我们的方法可以导致效率提高传统的设计，如随机设计，忽略了网络结构，我们说明了它的有用性的网络实验。
We propose a method for constructing optimal block designs for experiments on networks. The response model for a given network interference structure extends the linear network effects model to incorporate blocks. The optimality criteria are chosen to reflect the experimental objectives and an exchange algorithm is used to search across the design space for obtaining an efficient design when an exhaustive search is not possible. Our interest lies in estimating the direct comparisons among treatments, in the presence of nuisance network effects that stem from the underlying network interference structure governing the experimental units, or in the network effects themselves. Comparisons of optimal designs under different models, including the standard treatment models, are examined by comparing the variance and bias of treatment effect estimators. We also suggest a way of defining blocks, while taking into account the interrelations of groups of experimental units within a network, using spectral clustering techniques to achieve optimal modularity. We expect connected units within closed-form communities to behave similarly to an external stimulus. We provide evidence that our approach can lead to efficiency gains over conventional designs such as randomised designs that ignore the network structure and we illustrate its usefulness for experiments on networks.