Design-based edge-level causal inference with machine learning assisted covariate adjustment
机器学习辅助协变量调整的设计型边层面因果推断AI 解读 ↓
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AI 根据所提供的论文材料归类：研究直接讨论有向网络、二元干扰和边层面处理效应，属于网络环境中的因果推断。
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机器学习辅助协变量调整的设计型边层面因果推断
- 研究问题
- 当有向网络的边结果受两端个体处理状态共同影响时，如何估计边层面的因果效应，并处理共享节点引起的复杂依赖？
- 方法与思路
- 作者构造 Horvitz–Thompson 估计量和利用网络依赖的方差估计，采用三折分样与交叉拟合加入协变量，再通过校准控制效率损失。
- 材料陈述的贡献
- 摘要称建立渐近正态性、降低方差界的保守程度，并在稳定性条件下允许机器学习调整；校准保证相对未调整估计量无渐近效率损失。
- 阅读关注点
- 应核对二元干扰与效应的定义、随机分配设计、网络增长条件，以及三折分样为何恢复所需条件独立性和校准保证的适用范围。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未列出正则与稳定性条件，也未提供模拟和应用的具体结果。
原文摘要
We study design-based causal inference for edge-level outcomes in directed networks under dyadic interference. In this setting, outcomes are defined on directed edges and depend on the joint treatment assignments of pairs of units, inducing a complex dependence structure that invalidates standard estimation and inference procedures developed for node-level data. We construct Horvitz--Thompson estimators for a general class of edge-level causal effects and establish their asymptotic normality under mild regularity conditions. To enable valid inference, we develop variance estimators that exploit identifiable components of network dependence, yielding substantially less conservative bounds than classical approaches. To improve efficiency, we incorporate auxiliary covariates through a sample splitting and cross-fitting procedure. A key technical challenge is that standard two-fold sample splitting fails in the presence of edge-level outcomes due to the dependence induced by shared units. To address this issue, we introduce a three-fold sample splitting and cross-fitting scheme that restores the conditional independence required for unbiased estimation. Under a stability condition, the resulting covariate-adjusted estimator is asymptotically normal and accommodates both linear adjustment and flexible machine learning methods. We further introduce a calibration step that guarantees no asymptotic efficiency loss relative to the unadjusted estimator. Simulation studies and a real-data application confirm the theoretical results and demonstrate substantial efficiency gains.