用观测数据模拟干预，精准识别因果特征
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning
- 通过特征匹配模拟理想干预，构建潜在空间因果图
- 在分布外场景下表现优异，能准确区分因果与虚假特征
- 适合需要从纯观测数据中挖掘因果关系的研究者
从观测数据中进行因果发现的一大挑战是缺乏完美干预，难以区分因果特征与伪相关特征。我们提出一种创新方法——特征匹配干预（FMI），利用匹配过程模拟完美干预。我们定义了因果潜变量图，将结构化因果模型扩展至潜变量空间，为FMI与因果图学习提供理论框架。所提出的特征匹配过程在这些因果潜变量图内模拟完美干预。理论分析表明，FMI具有强分布外泛化能力。实验进一步验证其仅依赖观测数据即可有效识别因果特征，性能优于现有方法。
原文摘要 · Abstract (English)
A major challenge in causal discovery from observational data is the absence of perfect interventions, making it difficult to distinguish causal features from spurious ones. We propose an innovative approach, Feature Matching Intervention (FMI), which uses a matching procedure to mimic perfect interventions. We define causal latent graphs, extending structural causal models to latent feature space, providing a framework that connects FMI with causal graph learning. Our feature matching procedure emulates perfect interventions within these causal latent graphs. Theoretical results demonstrate that FMI exhibits strong out-of-distribution (OOD) generalizability. Experiments further highlight FMI's superior performance in effectively identifying causal features solely from observational data.
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