arXiv cs.AI· Zhirong Huang, Wenzhen Zhang, Guangquan Lu, Boyan Chen, Debo Cheng, Shichao Zhang·· 11 小时前AI 评分22
NEDNet：网络干扰下因果效应估计的网络效应分解方法
Network Effect Decomposition for Causal Effect Estimation under Network Interference
AI 导读
研究者提出 NEDNet，将条件结果响应参数化为查询不变的治疗前基线加上独立的治疗响应与暴露响应两条路径，并用路径特定的邻域上下文化为两条路径提供局部治疗前信息。在 BlogCatalog 和 Flickr 衍生的半合成基准上，NEDNet 在同质与协变量异质响应设置下，对个体效应、同伴效应和总效应均取得所比较方法中最低的 PEHE。
正文 · 原文
Abstract:Estimating causal effects from observational network data requires accounting for both individual treatment and neighborhood exposure. Existing outcome models commonly incorporate these intervention variables into a joint predictor and estimate causal effects through prediction contrasts, leaving the corresponding response components implicit. We propose the $\underline{N}$etwork $\underline{E}$ffect $\underline{D}$ecomposition $\underline{Net}$work (NEDNet), which parameterizes the conditional outcome response as a query-invariant pre-treatment baseline plus separate treatment- and exposure-response pathways. Path-specific neighborhood contextualization supplies local pre-treatment information to the two pathways. We characterize the fitted response through a unique reference-normalized decomposition, which is equivalent to a zero mixed treatment--exposure contrast. Experiments on semi-synthetic benchmarks derived from BlogCatalog and Flickr show that NEDNet achieves the lowest PEHE among the compared methods for individual, peer, and total effects across homogeneous and covariate-heterogeneous response settings.
| Subjects: | Machine Learning (stat.ML); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.07879 [stat.ML] |
| (or arXiv:2611.07879v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07879 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhirong Huang [view email]
[v1]
Thu, 5 Nov 2026 12:00:31 UTC (134 KB)
来源：arXiv cs.AI · arxiv.org