当干扰图演化时：动态同伴效应的双重稳健估计
When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects
- Adelaide University（阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对演化交互图中同伴效应估计难题，提出DynaNet-DR估计器，通过受控对比框架实现双重稳健性，半合成基准及MathOverflow观测研究验证了其良好估计精度。
AI中文摘要：
当交互图发生演化时，同伴效应难以估计，因为预先分配的网络历史、动态同伴暴露以及后分配的网络变化具有不同的因果作用。我们引入了一种受控对比框架，该框架通过自身处理、时间聚合的同伴暴露以及后分配演化摘要来索引潜在结果。所得均值之间的差异定义了自身处理、同伴暴露、受控网络演化以及联合受控对比，而非中介分解。我们开发了动态网络双重稳健估计器DynaNet-DR，其结合了时间分解的倾向评分与归一化增强。在一致性、摘要充分性、序贯可交换性、正性、干扰收敛以及弱依赖的假设下，当结果回归或倾向估计器一致时，其规范估计器是一致的。所报告的实现添加了代表性分数预测、固定截断以及有限样本稳定性。针对固定真实时间图序列的半合成基准测试显示，在针对完整轮廓的方法中，其估计精度良好。这些基准测试评估的是摘要索引对比，而非反事实边生成，而MathOverflow研究是在所述假设下的一项观测例证。
英文摘要：
Peer effects are difficult to estimate when interaction graphs evolve because pre-assignment network history, dynamic peer exposure, and post-assignment network change have distinct causal roles. We introduce a controlled contrast framework that indexes potential outcomes by own treatment, temporally aggregated peer exposure, and a post-assignment evolution summary. Differences between the resulting means define own-treatment, peer-exposure, controlled network-evolution, and joint controlled contrasts rather than a mediation decomposition. We develop the Dynamic Network Doubly Robust estimator, DynaNet-DR, which combines a temporally factorized propensity with normalized augmentation. Under consistency, summary sufficiency, sequential exchangeability, positivity, nuisance convergence, and weak dependence, its canonical estimator is consistent when either the outcome regression or the propensity estimator is consistent. The reported implementation adds representative-score prediction, fixed clipping, and finite-sample stabilization. Semi-synthetic benchmarks on fixed real temporal graph sequences show favorable estimation accuracy among methods targeting the full profile. These benchmarks assess summary-indexed contrasts rather than counterfactual edge generation, and the MathOverflow study is an observational illustration under the stated assumptions.
