AI 中文总结
针对干扰下因果效应估计问题，提出GAUGER框架，采用两步法，先利用强预测模型学习结果模式，再用图加权暴露水平残差化校准，所得估计器一致且方差可减，数值研究显示其比现有方法效率更高。
AI 中文摘要
在社会科学和经济学中，估计干扰下的因果效应是一个常见问题，但由于网络连接引起的复杂依赖结构，这具有挑战性。本文提出了GAUGER，一种基于图加权暴露水平残差化的广义回归调整框架，用于一般干扰下基于设计的因果推断。首先揭示了该设置下准确性和效率之间令人惊讶的不匹配：最小化预测误差的模型调整不一定导致处理效应估计器的最大方差减少。为解决此不匹配，提出两步法：利用强预测模型从网络和协变量中学习结果模式；应用称为图加权暴露水平残差化（GER）的新校准方案直接针对方差减少。所得估计器对于目标因果参数是一致的，具有可证明的方差减少，并且对于有效的统计推断具有保守方差估计的渐近正态性。作为该流程的实际实现，提出一种利用图神经网络（GNN）构建预测模型并使用GER引导模型调整以实现更好方差减少的方案。数值研究表明，与现有方法相比，效率有显著提高。
英文摘要
Estimating causal effects under interference is a common problem in social science and economics. However, it is challenging due to the complex dependency structure induced by network connections. In this paper, we propose GAUGER, a Generalized regression Adjustment framework via Graph-weighted Exposure-level Residualization for design-based causal inference under general interference. We first reveal a surprising mismatch between accuracy and efficiency in this setting: model adjustments that minimize prediction error (e.g., MSE) do not necessarily lead to the most variance reduction of the treatment effect estimator. To address this mismatch, we propose a two-step approach: (1) leveraging a strong prediction model to learn outcome patterns from the network and covariates, and (2) applying a novel calibration scheme called Graph-weighted Exposure-level Residualization (GER) that directly targets variance reduction. The resulting estimator is consistent for target causal parameters, enjoys provable variance reduction, and is asymptotically normal with a conservative variance estimator for valid statistical inference. As a practical implementation of the pipeline, we present a scheme that leverages Graph Neural Networks (GNNs) to construct the prediction model and use GER to steer the model adjustment for better variance reduction. Numerical studies show substantial efficiency gains over existing methods.
