A General Exposure-Mapping-Agnostic Framework for Causal Inference under Interference
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干扰下不依赖暴露映射的通用因果推断框架
- 研究问题
- 在网络上的两阶段集群随机实验中，如何不依赖暴露映射或排除跨集群干扰，识别网络因果效应并进行推断？
- 方法与思路
- 作者刻画能够识别网络效应的线性加权估计量，研究不同权重的渐近行为，并构造中心极限定理和保守方差估计。
- 材料陈述的贡献
- 摘要称部分估计量具有更好的理论与有限样本表现；特定子类的收敛率不依赖集群数，可达到单位总数的平方根率。
- 阅读关注点
- 应核对网络效应的定义、允许的干扰结构、平方根收敛率成立的条件，以及完全随机化下方差校正的具体构造。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未完整列出渐近条件，性能优势不能外推到所有网络与设计。
原文摘要
We develop a general framework for design-based causal inference under interference in cluster experiments conducted via two-stage randomization on a network of interconnected units, without relying on exposure mapping assumptions, exclusion of cross-cluster interference, or Bernoulli treatment assignments. Within this framework, we establish a complete characterization of linear weighted estimators (LW) as defined by Godambe (1955) that achieve identification of various network causal effects under interference. This general class includes several new estimators with improved theoretical guarantees and superior finite-sample performance relative to existing approaches such as standard inverse-probability-of-treatment weighting. For most estimators in this class, we establish central limit theorems and conservative variance estimators, which allows us to describe the distinct asymptotic behavior exhibited by different weighting schemes potentially of interest. In particular, we study how randomization at the cluster-level affects the asymptotic behavior of various estimators, and we identify a subclass of cluster-agnostic LW estimators whose convergence rates are independent of the number of clusters and attain the optimal root-N rate, where N denotes the total number of units. Notably, for complete randomization we develop new techniques that may be of independent interest, both to establish a central limit theorem for sums of general dependent statistics and to construct conservative and bias-corrected variance estimators. We complement our theoretical results with extensive simulation studies that offer practical guidance on the choice of weighting method and experimental design under a wide range of interference structures.