Auto-Doubly Robust Estimation of Causal Effects on a Network
网络因果效应的自回归双重稳健估计AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究网络中的处理干扰、溢出效应、邻域干预和依赖结构，支持因果推断与网络干扰分类。
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网络因果效应的自回归双重稳健估计
- 研究问题
- 研究单一大型网络的观察数据中，如何同时应对单位间长程依赖与一般干扰，识别并估计直接效应、溢出效应和网络干预结果。
- 方法与思路
- 作者构造网络版增广逆概率加权，结合网络倾向得分与结果模型，在条件可忽略性和两类 Markov 假设下建立相应估计方法。
- 材料陈述的贡献
- 摘要称给出邻域及全网络干预的双重稳健识别结果，证明弱网络依赖下的渐近正态性，并分析误设对方差的影响和监禁应用。
- 阅读关注点
- 应核对邻域距离与干预目标、两类 Markov 条件的并集含义，以及全网络识别所需的受限干扰、弱依赖和方差估计条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未给出假设和依赖强度的精确定义，双重稳健结论不覆盖任意网络误设。
原文摘要
This paper develops new methods for causal inference in observational studies on a single large network of interconnected units, addressing two key challenges: long-range dependence among units and the presence of general interference. We introduce a novel network version of Augmented Inverse Propensity Weighted, which combines propensity score and outcome models defined on the network to achieve doubly robust identification and estimation of both direct and spillover causal effects. Under a network version of conditional ignorability, the proposed approach identifies the expected potential outcome for a unit given the treatment assignment vector for its network neighborhood up to a user-specified distance, while marginalizing over treatment assignments for the rest of the network. Under the union of two Markov assumptions--one governing the propensity score model and the other the outcome model--we propose a corresponding semiparametric estimator based on general parametric specifications of nuisance functions. In particular, we suggest a class of parametric auto-regression models motivated by the Markov assumptions (Besag, 1974). By combining a restricted interference assumption with the propensity score model, we establish a new doubly robust identification result for the expected potential outcome under a hypothetical intervention on the treatment assignment vector for the entire network. We formally prove that, under weak network dependence, our proposed estimators are asymptotically normal and we characterize the impact of model misspecification on the asymptotic variance. Extensive simulation studies highlight the practical relevance of our approach. We further demonstrate its application in an empirical analysis of the NNAHRAY study, evaluating the impact of incarceration on individual socioeconomic outcomes in Brooklyn, New York.