Transportable Causal Effect Estimation across Networks under Interference

2026-08-19

AI Summary

研究解决了跨网络干扰下的因果效应迁移问题，通过扩展选择图并提出TranCE算法，结合干预结果模型、域密度比校正和交叉拟合推断方法。

Abstract

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.

Problem

• network interference
• causal effects
• data fusion
• transportability

Innovation

• Transportable Causal Effects
• Network Interference
• Selection Diagrams
• Doubly Robust Algorithm
• Covariate Shift