邻居处理效应异质性下网络干预的溢出效应

中文摘要 AI 辅助

优化预算约束下的网络干预，不仅需要评估个体的连接情况，还需预测单个接收者将处理收益传播的强度。现有模型从邻居的处理和属性预测溢出，本文指出溢出还取决于每个邻居对自身处理的响应强度。我们证明，仅独立汇总邻居处理和属性的模型无法捕捉这种交互作用，为此引入SpilloverNet，一种旨在保留邻居级响应动态的图神经网络。由于邻居的响应不可观测，自然的方法是从协变量估计后代入，但我们证明，仅依赖标准网络数据的任何预测器都会面临由不可观测的个人响应性设定的不可约误差下限。经验上，当异质性增长时，代入法的误差攀升至51.9%，比完全不使用响应性估计更差，但其整体相关性仍看似可接受。而在溢出发生前的小型预实验中收集的直接响应测量的单位级估计，可突破该下限，在预算定向问题中恢复多达14个百分点的最优福利。在两个真实社交图上，SpilloverNet达到7.7-8.4%的误差，优于标准图神经网络（GNNs）及专门的因果表示基线方法。

英文摘要

Optimising budget-constrained network interventions requires evaluating not just who is connected, but predicting how strongly individual recipients will propagate the treatment's benefits. Existing models predict spillover from neighbours' treatments and attributes. We argue that spillover also depends on how strongly each neighbour responded to its own treatment. We prove that no model in which neighbours' responsiveness enters separately from their treatments can capture this interaction, and we introduce \textsc{SpilloverNet}, a graph neural network designed to preserve neighbour-level response dynamics. Because a neighbour's response is unobserved, a natural approach is to estimate it from covariates and plug it in. However, we prove that any predictor relying solely on standard network data faces an irreducible error floor set by unobserved personal responsiveness. Empirically, at low heterogeneity the plug-in's correlation with the true response looks acceptable while its spillover error is already 24.5\% against an oracle 13.8\%; as heterogeneity grows its error climbs to 51.9\%, worse than using no responsiveness estimate at all. A per-unit estimate from a direct-response measurement, collected in a small pilot before spillover arrives, escapes this bound and cuts regret against oracle-$τ$ targeting by up to 14 percentage points in a budgeted targeting problem. On two real social graphs, \textsc{SpilloverNet} reaches 7.7--8.4\% error, outperforming standard GNNs as well as specialised causal-representation baselines.

发表机构

• University of Southern California（南加州大学）

机构由 AI 辅助整理，请以论文原文为准。