存在干扰时跨网络的可迁移因果效应估计
Transportable Causal Effect Estimation across Networks under Interference
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中文总结 AI 辅助
针对跨网络因果效应迁移的空白，提出TranCE双重鲁棒算法，经半合成基准与真实实地实验验证，可有效估计存在干扰时跨网络的可迁移因果效应。
中文摘要 AI 辅助
在网络干扰下估计因果效应的研究通常假设训练所用网络与部署所用网络一致。但实际场景中，干预措施会在一个群体中实施，而关注的问题涉及另一个群体，两者在拓扑结构、节点协变量组成和溢出路径上通常存在差异。因此，跨网络迁移因果效应是一个数据融合问题，尚无现有算法能解决。我们采用一种扩展至网络场景的选择图，将协变量偏移和网络结构偏移作为独立选择因子纳入，从中推导得出部署群体的直接效应、溢出效应和总效应的迁移公式，每个公式明确了需假设不变的干预机制以及必须重新加权的观测分布。随后，我们将这些公式转化为TranCE（Transported Causal Effects），这是一种结合了干预结果模型、领域密度比校正和交叉拟合推理的双重鲁棒算法。在两个源自真实社交网络的半合成基准数据集，以及一个完全真实的天气保险实地实验中，我们对迁移效应与保留的随机估计值进行了对比，大量实验结果证实了我们方法的有效性。研究结果有望改进网络系统，特别是社交网络和公共卫生领域的干预策略。
英文摘要
Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide. In practice, an intervention is run on one population while the question of interest concerns a different population, and the two generally differ in topology, node-covariate composition, and spillover pathways. Transporting a causal effect across networks is therefore a data-fusion problem that no existing algorithm solves. We employ a selection diagram, extended to the network setting so that covariate shift and structural network shift enter as separate selectors, and derive from it a transport formula for the direct, spillover, and total effects in the deployment population. Each formula makes explicit which interventional mechanism is assumed invariant and which observational distribution must be reweighted. We then turn the formulas into TranCE (Transported Causal Effects), a doubly-robust algorithm combining an interventional outcome model, a domain density-ratio correction, and cross-fitted inference. Extensive experiments on two semi-synthetic benchmarks derived from real-world social networks and on a fully real weather-insurance field experiment, where the transported effects are checked against held-out randomized estimates, confirm the effectiveness of our approach. Our findings have the potential to improve intervention strategies in networked systems, particularly in social networks and public health.
