Low-rank Covariate Balancing Estimators under Interference
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AI 根据所提供的论文材料归类：摘要明确研究单位间处理干扰、相关分配及相应因果效应估计。
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存在单位间干扰时的低秩协变量平衡估计
- 研究问题
- 当个体结果依赖他人处理且处理分配存在复杂相关性时，哪些干扰结构限制能够支持稳健的因果效应加权估计？
- 方法与思路
- 作者先讨论无干扰限制时的无偏估计边界，再引入潜在结果的低秩结构，构造平衡权重并提出候选结构的数据驱动选择。
- 材料陈述的贡献
- 摘要称低秩结构可在不知道真实倾向得分时支持无偏估计，已知倾向得分时可改善效率，并建立有限样本与渐近性质。
- 阅读关注点
- 应核对低秩结构的精确定义、未知分配依赖下无偏性成立的条件、目标效应范围，以及结构选择对后续推断的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核验；摘要未说明完整可行条件，不能把未知倾向得分的稳健性作无条件解释。
原文摘要
A key methodological challenge in observational studies with interference between units is twofold: (1) each unit's outcome may depend on many others' treatments, and (2) treatment assignments may exhibit complex dependencies across units. We develop a general statistical framework for constructing robust causal effect estimators to address these challenges. We first show that, without restricting the patterns of interference, the standard inverse probability weighting (IPW) estimator is the only uniformly unbiased estimator when the propensity score is known. In contrast, no estimator has such a property if the propensity score is unknown. We then introduce a \emph{low-rank structure} of potential outcomes as a broad class of structural assumptions about interference. This framework encompasses common assumptions such as anonymous, nearest-neighbor, and additive interference, while flexibly allowing for more complex study-specific interference assumptions. Under this low-rank assumption, we show how to construct an unbiased weighting estimator for a large class of causal estimands. The proposed weighting estimator does not require knowledge of true propensity scores and is therefore robust to unknown treatment assignment dependencies that often exist in observational studies. If the true propensity score is known, we can obtain an unbiased estimator that is more efficient than the IPW estimator by leveraging a low-rank structure. We establish the finite sample and asymptotic properties of the proposed weighting estimator, develop a data-driven procedure to select among candidate low-rank structures, and validate our approach through simulation and empirical studies.