用高维计算编码网络结构，提升因果推断准确性
C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interference
- 基于高维计算思想，将网络结构信息编码融入匹配过程
- 在多个数据集上优于或持平现有方法，且运行速度提升近10倍
- 适合大规模、实时性要求高的因果分析场景
针对观测数据中因网络混淆导致的因果效应估计问题——即个体的处理分配和结果受其网络邻居影响，引发网络干扰。传统因果推断方法常忽略此类依赖关系，导致估计偏差。本文提出一种新型基于匹配的方法，利用高维计算原理有效编码并整合网络结构信息，从而更准确地识别可比个体，提升因果效应估计的可靠性。在多个基准数据集上的广泛实证评估表明，该方法性能优于或相当于当前最优方法，包括若干计算开销显著更高的深度学习模型。此外，该方法在保持精度的同时，实现近乎一个数量级的运行时间降低，特别适用于大规模或对时效性要求高的应用场景。
原文摘要 · Abstract (English)
We address the problem of estimating causal effects from observational data in the presence of network confounding, a setting where both treatment assignment and observed outcomes of individuals may be influenced by their neighbors within a network structure, resulting in network interference. Traditional causal inference methods often fail to account for these dependencies, leading to biased estimates. To tackle this challenge, we introduce a novel matching-based approach that utilizes principles from hyperdimensional computing to effectively encode and incorporate structural network information. This enables more accurate identification of comparable individuals, thereby improving the reliability of causal effect estimates. Through extensive empirical evaluation on multiple benchmark datasets, we demonstrate that our method either outperforms or performs on par with existing state-of-the-art approaches, including several recent deep learning-based models that are significantly more computationally intensive. In addition to its strong empirical performance, our method offers substantial practical advantages, achieving nearly an order-of-magnitude reduction in runtime without compromising accuracy, making it particularly well-suited for large-scale or time-sensitive application
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