Low-order outcomes and clustered designs: combining design and analysis for causal inference under network interference
低阶结果模型与聚类设计：网络干扰下结合设计和分析的因果推断AI 解读 ↓
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低阶结果模型与聚类设计：网络干扰下结合设计和分析的因果推断
- 研究问题
- 存在网络干扰时，能否结合低阶潜在结果模型与图聚类随机化设计，降低全局处理效应估计的方差并控制模型误设偏差？
- 方法与思路
- 作者为一般实验设计提出伪逆估计量，推导设计相关的偏差与方差界，并将这些界用于分析和选择图聚类随机化方案。
- 材料陈述的贡献
- 摘要称正确阶数下估计无偏，误设偏差可由模型接近程度约束；聚类设计下方差尺度可结合两类降方差策略的优势。
- 阅读关注点
- 应核对低阶模型的定义、一般设计下无偏性所需条件，以及方差界与候选聚类选择规则是否适用于实际实验网络。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出定理条件，方差尺度结论不等于所有场景均更优。
原文摘要
Variance reduction for causal inference in the presence of network interference is often achieved through either outcome modeling, typically analyzed under unit-randomized Bernoulli designs, or clustered experimental designs, typically analyzed without strong parametric assumptions. In this work, we study the intersection of these two approaches and make the following threefold contributions. First, we present an estimator of the total treatment effect (or global average treatment effect) in low-order outcome models when the data are collected under general experimental designs, generalizing previous results for Bernoulli designs. We refer to this estimator as the pseudoinverse estimator and give bounds on its bias and variance in terms of properties of the experimental design. Second, we evaluate these bounds for the case of Bernoulli graph cluster randomized (GCR) designs. Its variance scales like the smaller of the variance obtained by the estimator derived under a low-order assumption, and the variance obtained from cluster randomization, showing that combining these variance reduction strategies is preferable to using either individually. When the order of the potential outcomes model is correctly specified, our estimator is always unbiased, and under a misspecified model, we upper bound the bias by the closeness of the ground truth model to a low-order model. Third, we give empirical evidence that our variance bounds can be used to select a good clustering that minimizes the worst-case variance under a cluster randomized design from a set of candidate clusterings. Across a range of graphs and clustering algorithms, our method consistently selects clusterings that perform well on a range of response models, suggesting the practical use of our bounds.