Modern causal decision-making increasingly demands individualized treatment-effect estimation in networks where interventions are high-dimensional, combinatorial vectors. While network interference, effect heterogeneity, and multi-dimensional treatments have been studied separately, their intersection yields an exponentially large intervention space that makes standard identification tools and low-dimensional exposure mappings untenable. We bridge this gap with a unified framework that constructs a \emph{global potential-outcome emulator} for unit-level inference. Our method combines (1) rooted network configurations to leverage local smoothness, (2) doubly robust orthogonalization to mitigate confounding from network position and covariates, and (3) sparse spectral learning to efficiently estimate response surfaces over the $2^p$-dimensional treatment space. We also decompose networked effects into own-treatment, structural, and interaction components, and provide finite-sample error bounds and asymptotic consistency guarantees. Overall, we show that individualized causal inference remains feasible in high-dimensional networked settings without collapsing the intervention space.

翻译：现代因果决策日益要求在干预为高维组合向量的网络中进行个体化处理效应估计。尽管网络干扰、效应异质性和多维处理已分别得到研究，但它们的交集产生了指数级庞大的干预空间，使得标准识别工具和低维暴露映射不再适用。我们通过构建用于单元级推断的\emph{全局潜在结果仿真器}的统一框架来弥合这一差距。该方法整合了：(1) 利用局部平滑性的根化网络配置，(2) 通过双重稳健正交化缓解网络位置与协变量带来的混杂偏倚，(3) 稀疏谱学习以高效估计$2^p$维处理空间上的响应曲面。我们还将网络效应分解为自身处理效应、结构效应与交互效应，并提供有限样本误差界与渐近一致性保证。总体而言，我们证明在不压缩干预空间的前提下，个体化因果推断在高维网络环境中依然具有可行性。